On hyperparameter optimization of machine learning algorithms: Theory and practice

Machine learning algorithms have been used widely in various applications and areas. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model’s performanc...

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Bibliographic Details
Published in	Neurocomputing (Amsterdam) Vol. 415; pp. 295 - 316
Main Authors	Yang, Li, Shami, Abdallah
Format	Journal Article
Language	English
Published	Elsevier B.V 20.11.2020
Subjects	Bayesian optimization Genetic algorithm Grid search Hyper-parameter optimization Machine learning Particle swarm optimization Grid search Hyper-parameter optimization Bayesian optimization Genetic algorithm Particle swarm optimization Machine learning
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Abstract	Machine learning algorithms have been used widely in various applications and areas. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model’s performance. It often requires deep knowledge of machine learning algorithms and appropriate hyper-parameter optimization techniques. Although several automatic optimization techniques exist, they have different strengths and drawbacks when applied to different types of problems. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization. This survey paper will help industrial users, data analysts, and researchers to better develop machine learning models by identifying the proper hyper-parameter configurations effectively.
AbstractList	Machine learning algorithms have been used widely in various applications and areas. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model’s performance. It often requires deep knowledge of machine learning algorithms and appropriate hyper-parameter optimization techniques. Although several automatic optimization techniques exist, they have different strengths and drawbacks when applied to different types of problems. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization. This survey paper will help industrial users, data analysts, and researchers to better develop machine learning models by identifying the proper hyper-parameter configurations effectively.
Author	Yang, Li Shami, Abdallah
Author_xml	– sequence: 1 givenname: Li surname: Yang fullname: Yang, Li email: lyang339@uwo.ca – sequence: 2 givenname: Abdallah surname: Shami fullname: Shami, Abdallah email: abdallah.shami@uwo.ca
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Cites_doi	10.1109/CEC.2014.6900618 10.1088/1749-4699/8/1/014008 10.1109/TSMC.1985.6313426 10.1109/79.543975 10.1111/j.2517-6161.1996.tb02080.x 10.1016/j.patcog.2005.06.013 10.1016/j.eswa.2017.11.028 10.1080/0952813X.2013.782347 10.1145/2806777.2806945 10.1109/ICADIWT.2014.6814687 10.1109/GLOCOM.2018.8647679 10.1109/ISEASP.2017.7976988 10.1016/j.is.2018.01.003 10.1007/978-3-540-74958-5_35 10.1109/21.97458 10.1007/978-3-540-31856-9_4 10.1016/j.knosys.2020.105992 10.1016/j.eswa.2017.02.017 10.1109/ACCESS.2018.2884225 10.1162/089976698300017007 10.1109/GLOCOM.2018.8647714 10.1109/ICCNC.2019.8685636 10.1049/iet-its.2018.5127 10.1007/978-3-030-05318-5_8 10.1147/JRD.2017.2709578 10.1162/089976600300015187 10.1007/BFb0040810 10.1186/s13321-017-0226-y 10.1109/GLOBECOM38437.2019.9013892 10.1080/08923647.2020.1696140 10.1109/JSEN.2005.858926 10.1080/00401706.1970.10488635 10.1609/aaai.v31i1.10647 10.14778/3007263.3007279 10.1155/2019/6278908 10.1007/s10618-005-0361-3 10.1016/j.proeng.2017.09.615 10.1007/s10044-007-0100-z 10.1109/ICDM.2017.137 10.1137/090771806 10.1007/s10489-020-01776-3 10.1016/j.camwa.2006.07.013 10.1142/S0129065704001899 10.21105/joss.00034 10.1109/CCECE47787.2020.9255697 10.1145/3071178.3071208 10.1061/(ASCE)CO.1943-7862.0001548 10.1007/978-3-7908-2604-3_16 10.1126/science.aaa8415 10.1561/2200000050
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References	Gambella, Ghaddar, Naoum-Sawaya (b0125) 2019 Hosmer, Lemeshow (b0190) 2013; 34 Gambella, Ghaddar, Naoum-Sawaya (b0165) 2019 T. Chen, C.Guestrin, XGBoost: a scalable tree boosting system, arXiv preprint arXiv:1603.02754, (2016). http://arxiv.org/abs/1603.02754. Zuo, Zhang, Wang (b0205) 2008; 11 Di Francescomarino, Dumas, Federici, Ghidini, Maggi, Rizzi, Simonetto (b0330) 2018; 74 (b0030) 2019 V. Narayanan, I. Arora, A. Bhatia, Fast and accurate sentiment classification using an enhanced naíve Bayes model, arXiv preprint arXiv:1305.6143, (2013). https://arxiv.org/abs/1305.6143. Bubeck (b0110) 2015; 8 Ding, He (b0340) 2004; 3056 Rasoul, David (b0260) 1991; 21 Moubayed, Injadat, Shami, Lutfiyya (b0335) 2020; 34 Ozaki, Yano, Onishi (b0315) 2017; 9 F. Chollet, Keras, 2015. https://github.com/fchollet/keras. Halko, Martinsson, Tropp (b0375) 2011; 53 Zhou, Wang, Li (b0365) 2012; 9 M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv preprint arXiv:1603.04467, (2016). https://arxiv.org/abs1603.04467. Brahim-Belhouari, Bermak, Shi, Chan (b0350) 2005; 5 Moubayed, Aqeeli, Shami (bib634) 2020 Domhan, Springenberg, Hutter (b0310) 2015- (2015) M. Injadat, F. Salo, A.B. Nassif, A. Essex, A. Shami, Bayesian optimization with machine learning algorithms towards anomaly detection, 2018 IEEE Glob. Commun. Conf. (2018) 1–6. https://doi.org/10.1109/glocom.2018.8647714. Autonomio Talos [Computer software], 2019. http://github.com/autonomio/talos. O.S. Soliman, A.S. Mahmoud, A classification system for remote sensing satellite images using support vector machine with non-linear kernel functions, 2012 8th Int. Conf. Informatics Syst. INFOS 2012. (2012) BIO-181-BIO-187. J. Rapin, O. Teytaud, Nevergrad – a gradient-free optimization platform, 2018. https://GitHub.com/FacebookResearch/Nevergrad. Boehm, Surve, Tatikonda (b0625) 2016; 9 Bergstra, Komer, Eliasmith, Yamins, Cox (b0530) 2015; 8 N. Hansen, A. Auger, O. Mersmann, T. Tusar, D. Brockhoff, COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting, arXiv preprint arXiv:1603.08785, (2016). https://arxiv.org/abs1603.08785. Fortin, De Rainville, Gardner, Parizeau, Gagńe (b0585) 2012; 13 J.N. Sulzmann, J. Fürnkranz, E. Hüllermeier, On pairwise naive bayes classifiers, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 4701 LNAI (2007) 371-381. https://doi.org/10.1007/978-3-540-74958-5_35 Diaz, Fokoue-Nkoutche, Nannicini, Samulowitz (b0120) 2017; 61 I. Dewancker, M. McCourt, S. Clark, Bayesian Optimization Primer, (2015). URL: https://sigopt.com/static/pdf/SigOpt Bayesian Optimization Primer.pdf. Diaz, Fokoue-Nkoutche, Nannicini, Samulowitz (b0025) 2017; 61 Kramer (b0145) 2016 Caruana, Niculescu-Mizil (b0140) 2006; 148 J. Grandgirard, D. Poinsot, L. Krespi, J.P. Nénon, A.M. Cortesero, Osprey: Hyperparameter Optimization for Machine Learning, 103 (2002) 239–248. https://doi.org/10.21105/joss.00034. Injadat, Moubayed, Nassif, Shami (bib637) 2020 Zhang, Jin, Yang, Hauptmann (b0220) 2003; 2 Bradley, Hax (b0105) 1977 B. Shahriari, A. Bouchard-Côté, N. de Freitas, Unbounded Bayesian optimization via regularization, Proc. Artif. Intell. Statist., (2016) 1168–1176. Moon (b0345) 1996; 13 Han, Liu, Fan (b0325) 2018; 95 Q. Yao, et al., Taking Human out of Learning Applications: A Survey on Automated Machine Learning, arXiv preprint arXiv:1810.13306, (2018). http://arxiv.org/abs/1810.13306. Bishop (b0605) 1995 Seeger (b0425) 2004; 14 L. Bottou, Large-scale machine learning with stochastic gradient descent, in: Proceedings of the COMPSTAT, Springer, 2010, pp. 177–186. Yang, Shami (bib638) 2020 Melkumova, Shatskikh (b0180) 2017; 201 Franceschi, Donini, Frasconi, Pontil (b0580) 2017 Kibriya, Frank, Pfahringer, Holmes (b0245) 2004; 3339 B. Kazimipour, X. Li, A.K. Qin, A Review of Population Initialization Techniques for Evolutionary Algorithms, 2014 IEEE Congr. Evol. Comput. (2014) 2585–2592. https://doi.org/10.1109/CEC.2014.6900618. Yang (bib632) 2018 C.M. Bishop, Pattern Recognition and Machine Learning, 2006, Springer, ISBN: 978-0-387-31073-2. Cazzaniga, Nobile, Besozzi (b0520) 2015 Martinez-Cantin (b0525) 2015; 15 S. Abreu, Automated Architecture Design for Deep Neural Networks, arXiv preprint arXiv:1908.10714, (2019). http://arxiv.org/abs/1908.10714 Itano, De Abreu De, Sousa (b0475) 2018 Lorenzo, Nalepa, Kawulok, Ramos, Paster (b0095) 2017 Loog (b0380) 2006; 2 J. Nocedal, S. Wright, Numerical Optimization, 2006, Springer-Verlag, ISBN: 978-0-387-40065-5. Yang, Moubayed, Hamieh, Shami (b0270) 2019 Eggensperger, Feurer, Hutter, Bergstra, Snoek, Hoos, Leyton-Brown (b0070) 2013 Komer, Bergstra, Eliasmith (b0535) 2014 D. Maclaurin, D. Duvenaud, R.P. Adams, Gradient-based Hyperparameter Optimization through Reversible Learning, arXiv preprint arXiv:1502.03492, (2015). http://arxiv.org/abs/1502.03492 M. Pumperla, Hyperas, 2019. http://maxpumperla.com/hyperas/. Luo (b0050) 2016; 5 Yang, Muresan, Al-Dweik, Hadjileontiadis (b0215) 2018; 6 Kuhn, Johnson (b0020) 2013 Rahnamayan, Tizhoosh, Salama (b0485) 2007; 53 Ogutu, Schulz-Streeck, Piepho (b0195) 2012; 6 Yang, Amari (b0410) 1998; 10 Hensman, Fusi, Lawrence (b0440) 2013 Z. Y., K. G., Hierarchical clustering algorithms for document dataset, Data Min. Knowl. Discov. 10 (2005) 141–168. K. Arjunan, C.N. Modi, An enhanced intrusion detection framework for securing network layer of cloud computing, ISEA Asia Secur. Priv. Conf. 2017, ISEASP 2017. (2017) 1–10. doi: 10.1109/ISEASP.2017.7976988. Krizhevsky, Sutskever, Hinton (b0610) 2012; 25 Pedregosa (b0150) 2011; 12 S. Sun, Z. Cao, H. Zhu, J. Zhao, A Survey of Optimization Methods from a Machine Learning Perspective, arXiv preprint arXiv:1906.06821, (2019). https://arxiv.org/abs/1906.06821. Cawley, Talbot (b0620) 2010; 11 Hutter, Hoos, Leyton-Brown (b0430) 2011; 5 Yan, He, Chen (b0500) 2017; 32 S. Lessmann, R. Stahlbock, S.F. Crone, Optimizing hyperparameters of support vector machines by genetic algorithms, Proc. 2005 Int. Conf. Artif. Intell. ICAI’05. 1 (2005) 74–80. Decastro-García, Muñoz Castañeda, Escudero García, Carriegos (b0035) 2019 (2019). Jordan, Mitchell (b0005) 2015; 349 Knudde, van der Herten, Dhaene, Couckuyt (b0555) 2017 S. Sanders, C. Giraud-Carrier, Informing the use of hyperparameter optimization through metalearning, Proc. – IEEE Int. Conf. Data Mining, ICDM. 2017-Novem (2017) 1051–1056. https://doi.org/10.1109/ICDM.2017.137 Keller, Gray (b0200) 1985; SMC-15 Wang, Wu, Wang, Dong, Yu, Chen (b0510) 2009; 2009 J. Shlens, A Tutorial on Principal Component Analysis, arXiv preprint arXiv:1404.1100, (2014). https://arxiv.org/abs1404.1100 I. Ilievski, T. Akhtar, J. Feng, C.A. Shoemaker, Efficient hyperparameter optimization of deep learning algorithms using deterministic RBF surrogates, 31st AAAI Conf. Artif. Intell. AAAI 2017, 2017, pp. 822–829 Injadat, Moubayed, Nassif, Shami (b0600) 2020; 200 Tibshirani (b0185) 1996; 58 R.S. Olson, J.H. Moore, TPOT: a tree-based pipeline optimization tool for automating machine learning, Auto Mach. Learn. (2019) 151–160. https://doi.org/10.1007/978-3-030-05318-5_8 Zhang, Xu, Huang, Chen (b0455) 2016- (2016) Bergstra, Bardenet, Bengio, Kégl (b0060) 2011 Y. Shi, R.C. Eberhart, Parameter Selection in Particle Swarm Optimization, Evolutionary Programming VII, Springer, 1998, pp. 591–600. M.-A. Zöller, M.F. Huber, Benchmark and Survey of Automated Machine Learning Frameworks, arXiv preprint arXiv:1904.12054, (2019). https://arxiv.org/abs/1904.12054 Cheng, Huang, Hutomo (b0505) 2018; 144 W. Yin, K. Kann, M. Yu, H. Schütze, Comparative Study of CNN and RNN for Natural Language Processing, arXiv preprint arXiv:1702.01923, (2017). https://arxiv.org/abs1702.01923. Li, Jamieson, DeSalvo, Rostamizadeh, Talwalkar (b0080) 2012; 18 Moubayed, Injadat, Shami, Lutfiyya (bib631) 2018 Lobo, Goldberg, Pelikan (b0490) 2000 Meng, Bradley, Yavuz (b0630) 2016; 17 K. Khan, S.U. Rehman, K. Aziz, S. Fong, S. Sarasvady, A. Vishwa, DBSCAN: Past, present and future, 5th Int. Conf. Appl. Digit. Inf. Web Technol. ICADIWT 2014, 2014, pp. 232–238. https://doi.org/10.1109/ICADIWT.2014.6814687. R.E. Shawi, M. Maher, S. Sakr, Automated machine learning: State-of-the-art and open challenges, arXiv preprint arXiv:1906.02287, (2019). http://arxiv.org/abs/1906.02287 Soon, Khaw, Chuah, Kanesan (b0320) 2018; 12 J. Snoek, H. Larochelle, R. Adams, Practical Bayesian optimization of machine learning algorithms, Adv. Neural Inf. Process. Syst. 4 (2012) 2951–2959. Dietterich (b0295) 1857; 2000 Bengio (b0405) 2000; 12 Hazan, Klivans, Yuan (b0420) 2017 Claesen, De Moor (b0445) 2015 Claesen, Simm, Popovic, Moreau, De Moor (b0395) 2014 Smola, Vapnik (b0210) 1997; 9 Sparks, Talwalkar, Haas, Franklin, Jordan, Kraska (b0130) 2015 Falkner, Klein, Hutter (b0465) 2018 Manias, Jammal, Hawilo, Shami, Heidari, Larabi, Brunner (b0265) 2019 Gogna, Tayal (b0470) 2013; 25 J.D.M. Rennie, L. Shih, J. Teevan, D.R. Karger Tackling the poor assumptions of Naive Bayes text classifiers, Proc. Twent. Int. Conf. Mach. Learn. ICML (2003), 616–623. Tim Head, MechCoder, Gilles Louppe, et al., scikitoptimize/scikit-optimize: v0.5.2, 2018. doi: 10.5281/zenodo.1207017. James, Yoshua (b0065) 2012; 13 L. Hertel, P. Sadowski, J. Collado, P. Baldi, Sherpa: hyperparameter optimization for machine learning models, Conf. Neural Inf. Process. Syst., 2018. Hoerl, Kennard (b0175) 1970; 12 M. Lindauer, K. Eggensperger, M. Feurer, S. Falkner, A. Biedenkapp, and F. Hutter, Smac v3: Algorithm configuration in python, 2017. https://github.com/automl/SMAC3. O.S. Steinholtz, A Comparative Study of Black-box Optimization Algorithms for Tuning of Hyper-parameters in Deep Neural Networks, M.S. thesis, Dept. Elect. Eng., Luleå Univ. Technol., 2018 Koutsoukas, Monaghan, Li, Huan (b0305) 2017; 9 Rish (b0230) 2001 Xia, Liu, Li, Liu (b0290) 2017; 78 Howland, Wang, Park (b0385) 2006; 39 Eggensperger, Hutter, Hoos, Leyton-Brown (b0075) 2015; 2 J. Wang, J. Xu, and X. Wang, Combination of Hyperband and Bayesian Optimization for Hype Kramer (10.1016/j.neucom.2020.07.061_b0145) 2016 10.1016/j.neucom.2020.07.061_b0285 10.1016/j.neucom.2020.07.061_b0560 10.1016/j.neucom.2020.07.061_b0045 10.1016/j.neucom.2020.07.061_b0280 10.1016/j.neucom.2020.07.061_b0160 10.1016/j.neucom.2020.07.061_b0040 Bradley (10.1016/j.neucom.2020.07.061_b0105) 1977 Hensman (10.1016/j.neucom.2020.07.061_b0440) 2013 10.1016/j.neucom.2020.07.061_b0565 Rasoul (10.1016/j.neucom.2020.07.061_b0260) 1991; 21 Tibshirani (10.1016/j.neucom.2020.07.061_b0185) 1996; 58 Koutsoukas (10.1016/j.neucom.2020.07.061_b0305) 2017; 9 Di Francescomarino (10.1016/j.neucom.2020.07.061_b0330) 2018; 74 Zhang (10.1016/j.neucom.2020.07.061_b0220) 2003; 2 Knudde (10.1016/j.neucom.2020.07.061_b0555) 2017 Manias (10.1016/j.neucom.2020.07.061_b0265) 2019 Cazzaniga (10.1016/j.neucom.2020.07.061_b0520) 2015 Luo (10.1016/j.neucom.2020.07.061_b0050) 2016; 5 10.1016/j.neucom.2020.07.061_b0275 Bergstra (10.1016/j.neucom.2020.07.061_b0530) 2015; 8 10.1016/j.neucom.2020.07.061_b0550 10.1016/j.neucom.2020.07.061_b0155 Ding (10.1016/j.neucom.2020.07.061_b0340) 2004; 3056 10.1016/j.neucom.2020.07.061_b0390 Melkumova (10.1016/j.neucom.2020.07.061_b0180) 2017; 201 10.1016/j.neucom.2020.07.061_b0435 Lorenzo (10.1016/j.neucom.2020.07.061_b0095) 2017 James (10.1016/j.neucom.2020.07.061_b0065) 2012; 13 Eggensperger (10.1016/j.neucom.2020.07.061_b0070) 2013 Zuo (10.1016/j.neucom.2020.07.061_b0205) 2008; 11 Yang (10.1016/j.neucom.2020.07.061_b0410) 1998; 10 Claesen (10.1016/j.neucom.2020.07.061_b0395) 2014 Claesen (10.1016/j.neucom.2020.07.061_b0445) 2015 Zhou (10.1016/j.neucom.2020.07.061_b0365) 2012; 9 Dietterich (10.1016/j.neucom.2020.07.061_b0295) 1857; 2000 Salo (10.1016/j.neucom.2020.07.061_bib633) 2019 10.1016/j.neucom.2020.07.061_b0100 Zhang (10.1016/j.neucom.2020.07.061_b0455) 2016 Bishop (10.1016/j.neucom.2020.07.061_b0605) 1995 Diaz (10.1016/j.neucom.2020.07.061_b0025) 2017; 61 Yang (10.1016/j.neucom.2020.07.061_bib632) 2018 10.1016/j.neucom.2020.07.061_b0225 Loog (10.1016/j.neucom.2020.07.061_b0380) 2006; 2 Rish (10.1016/j.neucom.2020.07.061_b0230) 2001 Bubeck (10.1016/j.neucom.2020.07.061_b0110) 2015; 8 Yang (10.1016/j.neucom.2020.07.061_b0270) 2019 Rahnamayan (10.1016/j.neucom.2020.07.061_b0485) 2007; 53 Bergstra (10.1016/j.neucom.2020.07.061_b0060) 2011 Sparks (10.1016/j.neucom.2020.07.061_b0130) 2015 Han (10.1016/j.neucom.2020.07.061_b0325) 2018; 95 Moubayed (10.1016/j.neucom.2020.07.061_b0335) 2020; 34 Cheng (10.1016/j.neucom.2020.07.061_b0505) 2018; 144 10.1016/j.neucom.2020.07.061_b0570 10.1016/j.neucom.2020.07.061_b0450 10.1016/j.neucom.2020.07.061_b0055 (10.1016/j.neucom.2020.07.061_b0030) 2019 Yang (10.1016/j.neucom.2020.07.061_bib638) 2020 10.1016/j.neucom.2020.07.061_b0170 Bustamante (10.1016/j.neucom.2020.07.061_b0240) 2006 Howland (10.1016/j.neucom.2020.07.061_b0385) 2006; 39 Seeger (10.1016/j.neucom.2020.07.061_b0425) 2004; 14 Eggensperger (10.1016/j.neucom.2020.07.061_b0075) 2015; 2 Smola (10.1016/j.neucom.2020.07.061_b0210) 1997; 9 10.1016/j.neucom.2020.07.061_b0575 Hosmer (10.1016/j.neucom.2020.07.061_b0190) 2013; 34 Domhan (10.1016/j.neucom.2020.07.061_b0310) 2015 Moon (10.1016/j.neucom.2020.07.061_b0345) 1996; 13 Moubayed (10.1016/j.neucom.2020.07.061_bib631) 2018 Wang (10.1016/j.neucom.2020.07.061_b0510) 2009; 2009 10.1016/j.neucom.2020.07.061_b0615 Injadat (10.1016/j.neucom.2020.07.061_b0600) 2020; 200 Yang (10.1016/j.neucom.2020.07.061_b0215) 2018; 6 Gambella (10.1016/j.neucom.2020.07.061_b0125) 2019 Karnin (10.1016/j.neucom.2020.07.061_b0460) 2013 Hoerl (10.1016/j.neucom.2020.07.061_b0175) 1970; 12 Hazan (10.1016/j.neucom.2020.07.061_b0420) 2017 Meng (10.1016/j.neucom.2020.07.061_b0630) 2016; 17 10.1016/j.neucom.2020.07.061_b0480 Cawley (10.1016/j.neucom.2020.07.061_b0620) 2010; 11 10.1016/j.neucom.2020.07.061_b0085 10.1016/j.neucom.2020.07.061_b0360 Caruana (10.1016/j.neucom.2020.07.061_b0140) 2006; 148 10.1016/j.neucom.2020.07.061_b0400 Pedregosa (10.1016/j.neucom.2020.07.061_b0150) 2011; 12 Xia (10.1016/j.neucom.2020.07.061_b0290) 2017; 78 Martinez-Cantin (10.1016/j.neucom.2020.07.061_b0525) 2015; 15 Li (10.1016/j.neucom.2020.07.061_b0080) 2012; 18 Injadat (10.1016/j.neucom.2020.07.061_bib637) 2020 Diaz (10.1016/j.neucom.2020.07.061_b0120) 2017; 61 Kibriya (10.1016/j.neucom.2020.07.061_b0245) 2004; 3339 10.1016/j.neucom.2020.07.061_b0595 10.1016/j.neucom.2020.07.061_b0590 Ogutu (10.1016/j.neucom.2020.07.061_b0195) 2012; 6 Halko (10.1016/j.neucom.2020.07.061_b0375) 2011; 53 Krizhevsky (10.1016/j.neucom.2020.07.061_b0610) 2012; 25 10.1016/j.neucom.2020.07.061_b0515 10.1016/j.neucom.2020.07.061_b0355 10.1016/j.neucom.2020.07.061_b0235 Soon (10.1016/j.neucom.2020.07.061_b0320) 2018; 12 10.1016/j.neucom.2020.07.061_b0115 Jordan (10.1016/j.neucom.2020.07.061_b0005) 2015; 349 Gambella (10.1016/j.neucom.2020.07.061_b0165) 2019 Fortin (10.1016/j.neucom.2020.07.061_b0585) 2012; 13 Lobo (10.1016/j.neucom.2020.07.061_b0490) 2000 Itano (10.1016/j.neucom.2020.07.061_b0475) 2018 Keller (10.1016/j.neucom.2020.07.061_b0200) 1985; SMC-15 Decastro-García (10.1016/j.neucom.2020.07.061_b0035) 2019 10.1016/j.neucom.2020.07.061_b0540 Yan (10.1016/j.neucom.2020.07.061_b0500) 2017; 32 10.1016/j.neucom.2020.07.061_b0545 10.1016/j.neucom.2020.07.061_b0300 Brahim-Belhouari (10.1016/j.neucom.2020.07.061_b0350) 2005; 5 Franceschi (10.1016/j.neucom.2020.07.061_b0580) 2017 10.1016/j.neucom.2020.07.061_b0090 Moubayed (10.1016/j.neucom.2020.07.061_bib634) 2020 10.1016/j.neucom.2020.07.061_b0010 Ozaki (10.1016/j.neucom.2020.07.061_b0315) 2017; 9 Gogna (10.1016/j.neucom.2020.07.061_b0470) 2013; 25 10.1016/j.neucom.2020.07.061_b0495 Hutter (10.1016/j.neucom.2020.07.061_b0430) 2011; 5 Falkner (10.1016/j.neucom.2020.07.061_b0465) 2018 10.1016/j.neucom.2020.07.061_b0370 10.1016/j.neucom.2020.07.061_b0250 Kuhn (10.1016/j.neucom.2020.07.061_b0020) 2013 10.1016/j.neucom.2020.07.061_b0415 Boehm (10.1016/j.neucom.2020.07.061_b0625) 2016; 9 10.1016/j.neucom.2020.07.061_b0255 10.1016/j.neucom.2020.07.061_b0135 10.1016/j.neucom.2020.07.061_b0015 Komer (10.1016/j.neucom.2020.07.061_b0535) 2014 Bengio (10.1016/j.neucom.2020.07.061_b0405) 2000; 12
References_xml	– volume: 8 year: 2015 ident: b0530 article-title: Hyperopt: a Python library for model selection and hyperparameter optimization publication-title: Comput. Sci. Discov. – volume: 58 start-page: 267 year: 1996 end-page: 288 ident: b0185 article-title: Regression shrinkage and selection via the Lasso publication-title: J. R. Stat. Soc. Ser. B – start-page: 481 year: 2017 end-page: 488 ident: b0095 article-title: Particle swarm optimization for hyper-parameter selection in deep neural networks publication-title: Proc. ACM Int. Conf. Genet. Evol. Comput. – reference: S. Sanders, C. Giraud-Carrier, Informing the use of hyperparameter optimization through metalearning, Proc. – IEEE Int. Conf. Data Mining, ICDM. 2017-Novem (2017) 1051–1056. https://doi.org/10.1109/ICDM.2017.137 – reference: Autonomio Talos [Computer software], 2019. http://github.com/autonomio/talos. – reference: B. Kazimipour, X. Li, A.K. Qin, A Review of Population Initialization Techniques for Evolutionary Algorithms, 2014 IEEE Congr. Evol. Comput. (2014) 2585–2592. https://doi.org/10.1109/CEC.2014.6900618. – start-page: 34 year: 2014 end-page: 40 ident: b0535 article-title: Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn publication-title: Proc. ICML Workshop AutoML – start-page: 1 year: 2013 end-page: 5 ident: b0070 article-title: Towards an empirical foundation for assessing Bayesian optimization of hyperparameters publication-title: BayesOpt Work – reference: I. Ilievski, T. Akhtar, J. Feng, C.A. Shoemaker, Efficient hyperparameter optimization of deep learning algorithms using deterministic RBF surrogates, 31st AAAI Conf. Artif. Intell. AAAI 2017, 2017, pp. 822–829 – reference: S. Lessmann, R. Stahlbock, S.F. Crone, Optimizing hyperparameters of support vector machines by genetic algorithms, Proc. 2005 Int. Conf. Artif. Intell. ICAI’05. 1 (2005) 74–80. – volume: 5 start-page: 507 year: 2011 end-page: 523 ident: b0430 article-title: Sequential model-based optimization for general algorithm configuration publication-title: Proc. LION – reference: Z. Y., K. G., Hierarchical clustering algorithms for document dataset, Data Min. Knowl. Discov. 10 (2005) 141–168. – year: 2020 ident: bib637 article-title: Multi-split optimized bagging ensemble model selection for multi-class educational data mining publication-title: Springer’s Appl. Intell. – reference: J.N. Sulzmann, J. Fürnkranz, E. Hüllermeier, On pairwise naive bayes classifiers, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 4701 LNAI (2007) 371-381. https://doi.org/10.1007/978-3-540-74958-5_35 – volume: 53 start-page: 217 year: 2011 end-page: 288 ident: b0375 article-title: Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions publication-title: SIAM Rev. – reference: J. Wang, J. Xu, and X. Wang, Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning, arXiv preprint arXiv:1801.01596, (2018). https://arxiv.org/abs1801.01596. – year: 2019 ident: b0030 publication-title: Automatic Machine Learning: Methods, Systems, Challenges – volume: 2 start-page: 888 year: 2003 end-page: 895 ident: b0220 article-title: Modified logistic regression: an approximation to SVM and its applications in large-scale text categorization publication-title: Proceedings Twent. Int. Conf. Mach. Learn. – reference: N. Hansen, A. Auger, O. Mersmann, T. Tusar, D. Brockhoff, COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting, arXiv preprint arXiv:1603.08785, (2016). https://arxiv.org/abs1603.08785. – year: 2013 ident: b0440 article-title: Gaussian processes for big data, arXiv preprint arXiv:1309.6835 – start-page: 45 year: 2016 end-page: 53 ident: b0145 article-title: Scikit-Learn, in Machine Learning for Evolution Strategies – reference: L. Bottou, Large-scale machine learning with stochastic gradient descent, in: Proceedings of the COMPSTAT, Springer, 2010, pp. 177–186. – start-page: 1 year: 2015 end-page: 8 ident: b0520 article-title: The impact of particles initialization in PSO: parameter estimation as a case in point publication-title: 2015 IEEE Conf. Comput. Intell. Bioinforma. Comput. Biol. CIBCB 2015 – volume: 34 start-page: 358 year: 2013 end-page: 359 ident: b0190 article-title: Applied logistic regression publication-title: Technometrics – year: 2015 ident: b0445 article-title: Hyperparameter Search in Machine Learning, arXiv preprint arXiv:1502.02127 – volume: 8 start-page: 231 year: 2015 end-page: 357 ident: b0110 article-title: Convex optimization: algorithms and complexity publication-title: Found. Trends Mach. Learn. – reference: B. Shahriari, A. Bouchard-Côté, N. de Freitas, Unbounded Bayesian optimization via regularization, Proc. Artif. Intell. Statist., (2016) 1168–1176. – reference: D. Maclaurin, D. Duvenaud, R.P. Adams, Gradient-based Hyperparameter Optimization through Reversible Learning, arXiv preprint arXiv:1502.03492, (2015). http://arxiv.org/abs/1502.03492 – year: 2013 ident: b0020 article-title: Applied Predictive Modeling – reference: R.E. Shawi, M. Maher, S. Sakr, Automated machine learning: State-of-the-art and open challenges, arXiv preprint arXiv:1906.02287, (2019). http://arxiv.org/abs/1906.02287 – reference: O.S. Steinholtz, A Comparative Study of Black-box Optimization Algorithms for Tuning of Hyper-parameters in Deep Neural Networks, M.S. thesis, Dept. Elect. Eng., Luleå Univ. Technol., 2018 – volume: 12 start-page: 69 year: 1970 end-page: 82 ident: b0175 article-title: Ridge regression: applications to nonorthogonal problems publication-title: Technometrics – volume: 6 year: 2012 ident: b0195 article-title: Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions publication-title: BMC Proc. BioMed Cent. – volume: 9 year: 2017 ident: b0315 article-title: Effective hyperparameter optimization using Nelder-Mead method in deep learning publication-title: IPSJ Trans. Comput. Vis. Appl. – start-page: 237 year: 2006 end-page: 247 ident: b0240 article-title: Comparing fuzzy Naive Bayes and Gaussian Naive Bayes for decision making in RoboCup 3D publication-title: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) – year: 2019 (2019). ident: b0035 article-title: Effect of the sampling of a dataset in the hyperparameter optimization phase over the efficiency of a machine learning algorithm publication-title: Complexity – volume: 2 start-page: 387 year: 2006 end-page: 390 ident: b0380 article-title: Conditional linear discriminant analysis publication-title: Proc. – Int. Conf. Pattern Recognit. – volume: 10 start-page: 2137 year: 1998 end-page: 2157 ident: b0410 article-title: Complexity issues in natural gradient descent method for training multilayer perceptrons publication-title: Neural Comput. – volume: 61 start-page: 1 year: 2017 end-page: 20 ident: b0120 article-title: An effective algorithm for hyperparameter optimization of neural networks publication-title: IBM J. Res. Dev. – reference: K. Khan, S.U. Rehman, K. Aziz, S. Fong, S. Sarasvady, A. Vishwa, DBSCAN: Past, present and future, 5th Int. Conf. Appl. Digit. Inf. Web Technol. ICADIWT 2014, 2014, pp. 232–238. https://doi.org/10.1109/ICADIWT.2014.6814687. – volume: 39 start-page: 277 year: 2006 end-page: 287 ident: b0385 article-title: Solving the small sample size problem in face recognition using generalized discriminant analysis publication-title: Pattern Recognit. – reference: S. Sun, Z. Cao, H. Zhu, J. Zhao, A Survey of Optimization Methods from a Machine Learning Perspective, arXiv preprint arXiv:1906.06821, (2019). https://arxiv.org/abs/1906.06821. – start-page: 2275 year: 2013 end-page: 2283 ident: b0460 article-title: Almost optimal exploration in multi-armed bandits publication-title: 30th Int. Conf. Mach. Learn. ICML 2013 – start-page: 1 year: 2019 end-page: 40 ident: b0165 publication-title: Optimization Models for Machine Learning: A Survey – reference: J. Shlens, A Tutorial on Principal Component Analysis, arXiv preprint arXiv:1404.1100, (2014). https://arxiv.org/abs1404.1100 – reference: S. Abreu, Automated Architecture Design for Deep Neural Networks, arXiv preprint arXiv:1908.10714, (2019). http://arxiv.org/abs/1908.10714 – reference: J. Snoek, H. Larochelle, R. Adams, Practical Bayesian optimization of machine learning algorithms, Adv. Neural Inf. Process. Syst. 4 (2012) 2951–2959. – reference: V. Narayanan, I. Arora, A. Bhatia, Fast and accurate sentiment classification using an enhanced naíve Bayes model, arXiv preprint arXiv:1305.6143, (2013). https://arxiv.org/abs/1305.6143. – volume: 78 start-page: 225 year: 2017 end-page: 241 ident: b0290 article-title: A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring publication-title: Expert Syst. Appl. – volume: 34 start-page: 1 year: 2020 end-page: 20 ident: b0335 article-title: Student engagement level in e-learning environment: clustering using K-means publication-title: Am. J. Distance Educ. – volume: 15 start-page: 3735 year: 2015 end-page: 3739 ident: b0525 article-title: BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits publication-title: J. Mach. Learn. Res. – year: 2020 ident: bib634 article-title: Ensemble-based feature selection and classification model for DNS typo-squatting detection publication-title: 2020 IEEE Can. Conf. Electr. Comput. Eng. – volume: 2 start-page: 1114 year: 2015 end-page: 1120 ident: b0075 article-title: Efficient benchmarking of hyperparameter optimizers via surrogates publication-title: Proc. Natl. Conf. Artif. Intell. – start-page: 670 year: 2016- (2016) end-page: 674 ident: b0455 article-title: A new optimal sampling rule for multi-fidelity optimization via ordinal transformation publication-title: IEEE Int. Conf. Autom. Sci. Eng. – reference: J. Rapin, O. Teytaud, Nevergrad – a gradient-free optimization platform, 2018. https://GitHub.com/FacebookResearch/Nevergrad. – volume: 9 start-page: 1967 year: 2012 end-page: 1973 ident: b0365 article-title: Research on adaptive parameters determination in DBSCAN algorithm publication-title: J. Inf. Comput. Sci. – start-page: 151 year: 2000 end-page: 158 ident: b0490 article-title: Time complexity of genetic algorithms on exponentially scaled problems publication-title: Proc. Genet. Evol. Comput. Conf. – reference: F. Chollet, Keras, 2015. https://github.com/fchollet/keras. – year: 1995 ident: b0605 article-title: Neural Networks for Pattern Recognition – volume: 25 start-page: 1097 year: 2012 end-page: 1105 ident: b0610 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural Inf. Process. Syst. – reference: L. Hertel, P. Sadowski, J. Collado, P. Baldi, Sherpa: hyperparameter optimization for machine learning models, Conf. Neural Inf. Process. Syst., 2018. – volume: 5 start-page: 1 year: 2016 end-page: 16 ident: b0050 article-title: A review of automatic selection methods for machine learning algorithms and hyper-parameter values publication-title: Netw. Model. Anal. Heal. Inf. Bioinf. – volume: 11 start-page: 2079 year: 2010 end-page: 2107 ident: b0620 article-title: On over-fitting in model selection and subsequent selection bias in performance evaluation publication-title: J. Mach. Learn. Res. – volume: 2009 start-page: 332 year: 2009 end-page: 336 ident: b0510 article-title: A new population initialization method based on space transformation search, 5th Int publication-title: Conf. Nat. Comput. ICNC – reference: J. Nocedal, S. Wright, Numerical Optimization, 2006, Springer-Verlag, ISBN: 978-0-387-40065-5. – year: 2014 ident: b0395 publication-title: Easy Hyperparameter Search Using Optunity, arXiv preprint arXiv:1412.1114 – volume: 14 start-page: 69 year: 2004 end-page: 106 ident: b0425 article-title: Gaussian processes for machine learning publication-title: Int. J. Neural Syst. – start-page: 368 year: 2015 end-page: 380 ident: b0130 article-title: Automating model search for large scale machine learning publication-title: Proc. 6th ACM Symp. Cloud Comput. – volume: 12 start-page: 1889 year: 2000 end-page: 1900 ident: b0405 article-title: Gradient-based optimization of hyperparameters publication-title: Neural Comput. – volume: 13 start-page: 281 year: 2012 end-page: 305 ident: b0065 article-title: Random search for hyper-parameter optimization publication-title: J. Mach. Learn. Res. – reference: K. Arjunan, C.N. Modi, An enhanced intrusion detection framework for securing network layer of cloud computing, ISEA Asia Secur. Priv. Conf. 2017, ISEASP 2017. (2017) 1–10. doi: 10.1109/ISEASP.2017.7976988. – year: 2020 ident: bib638 publication-title: Hyperparameter Optimization of Machine Learning Algorithms – reference: M.-A. Zöller, M.F. Huber, Benchmark and Survey of Automated Machine Learning Frameworks, arXiv preprint arXiv:1904.12054, (2019). https://arxiv.org/abs/1904.12054 – volume: 148 start-page: 161 year: 2006 end-page: 168 ident: b0140 article-title: An empirical comparison of supervised learning algorithms publication-title: ACM Int. Conf. Proc. Ser. – start-page: 2323 year: 2018 end-page: 2341 ident: b0465 article-title: BOHB: robust and efficient hyperparameter optimization at scale publication-title: 35th Int. Conf. Mach. Learn. ICML 2018 – year: 2018 ident: bib632 publication-title: Comprehensive visibility indicator algorithm for adaptable speed limit control in intelligent transportation systems – reference: O.S. Soliman, A.S. Mahmoud, A classification system for remote sensing satellite images using support vector machine with non-linear kernel functions, 2012 8th Int. Conf. Informatics Syst. INFOS 2012. (2012) BIO-181-BIO-187. – volume: 12 start-page: 939 year: 2018 end-page: 946 ident: b0320 article-title: Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition publication-title: IET Intell. Transp. Syst. – reference: M. Injadat, F. Salo, A.B. Nassif, A. Essex, A. Shami, Bayesian optimization with machine learning algorithms towards anomaly detection, 2018 IEEE Glob. Commun. Conf. (2018) 1–6. https://doi.org/10.1109/glocom.2018.8647714. – volume: 144 start-page: 1 year: 2018 end-page: 7 ident: b0505 article-title: Multiobjective dynamic-guiding PSO for optimizing work shift schedules publication-title: J. Constr. Eng. Manag. – year: 2019 ident: b0125 article-title: Optimization Models for Machine Learning: A Survey, arXiv preprint arXiv:1901.05331 – volume: SMC-15 start-page: 580 year: 1985 end-page: 585 ident: b0200 article-title: A fuzzy K-nearest neighbor algorithm publication-title: IEEE Trans. Syst. Man Cybern. – start-page: 41 year: 2001 end-page: 46 ident: b0230 article-title: An empirical study of the naive Bayes classifier publication-title: IJCAI 2001 Work Empir. Methods Artif. Intell. – year: 2019 ident: b0270 article-title: Tree-based intelligent intrusion detection system in internet of vehicles publication-title: 2019 IEEE Glob. Commun. Conf. GLOBECOM 2019 – Proc. – reference: R.S. Olson, J.H. Moore, TPOT: a tree-based pipeline optimization tool for automating machine learning, Auto Mach. Learn. (2019) 151–160. https://doi.org/10.1007/978-3-030-05318-5_8 – volume: 9 start-page: 155 year: 1997 end-page: 161 ident: b0210 article-title: Support vector regression machines publication-title: Adv. Neural Inf. Process. Syst. – year: 1977 ident: b0105 article-title: Applied Mathematical Programming – volume: 2000 start-page: 1 year: 1857 end-page: 15 ident: b0295 article-title: Ensemble methods in machine learning publication-title: Mult. Classif. Syst. – start-page: 12 year: 2019 end-page: 17 ident: b0265 article-title: Machine learning for performance-aware virtual network function placement publication-title: 2019 IEEE Glob. Commun. Conf. GLOBECOM 2019 – Proc. – volume: 5 start-page: 1433 year: 2005 end-page: 1444 ident: b0350 article-title: Fast and Robust gas identification system using an integrated gas sensor technology and Gaussian mixture models publication-title: IEEE Sens. J. – volume: 61 start-page: 1 year: 2017 end-page: 20 ident: b0025 article-title: An effective algorithm for hyperparameter optimization of neural networks publication-title: IBM J. Res. Dev. – reference: T. Chen, C.Guestrin, XGBoost: a scalable tree boosting system, arXiv preprint arXiv:1603.02754, (2016). http://arxiv.org/abs/1603.02754. – volume: 74 start-page: 67 year: 2018 end-page: 83 ident: b0330 article-title: Genetic algorithms for hyperparameter optimization in predictive business process monitoring publication-title: Inf. Syst. – volume: 13 start-page: 2171 year: 2012 end-page: 2175 ident: b0585 article-title: DEAP: evolutionary algorithms made easy publication-title: J. Mach. Learn. Res. – reference: J. Grandgirard, D. Poinsot, L. Krespi, J.P. Nénon, A.M. Cortesero, Osprey: Hyperparameter Optimization for Machine Learning, 103 (2002) 239–248. https://doi.org/10.21105/joss.00034. – volume: 95 start-page: 43 year: 2018 end-page: 56 ident: b0325 article-title: A new image classification method using CNN transfer learning and web data augmentation publication-title: Expert Syst. Appl. – year: 2017 ident: b0555 publication-title: GPflowOpt: A Bayesian Optimization Library using TensorFlow, arXiv preprint arXiv:1711.03845 – reference: Tim Head, MechCoder, Gilles Louppe, et al., scikitoptimize/scikit-optimize: v0.5.2, 2018. doi: 10.5281/zenodo.1207017. – volume: 3056 start-page: 414 year: 2004 end-page: 418 ident: b0340 article-title: Cluster structure of K-means clustering via principal component analysis publication-title: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) – start-page: 2546 year: 2011 end-page: 2554 ident: b0060 article-title: Algorithms for hyper-parameter optimization publication-title: Proc. Adv. Neural Inf. Process. Syst. – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: b0150 article-title: Scikit-learn: machine learning in Python publication-title: J. Mach. Learn. Res. – reference: I. Dewancker, M. McCourt, S. Clark, Bayesian Optimization Primer, (2015). URL: https://sigopt.com/static/pdf/SigOpt Bayesian Optimization Primer.pdf. – volume: 21 start-page: 660 year: 1991 end-page: 674 ident: b0260 article-title: A survey of decision tree classifier methodology publication-title: IEEE Trans. Syst. Man. Cybern. – year: 2017 ident: b0420 article-title: Hyperparameter optimization: a spectral approach, arXiv preprint arXiv:1706.00764 – volume: 349 start-page: 255 year: 2015 end-page: 260 ident: b0005 article-title: Machine learning: trends, perspectives, and prospects publication-title: Science – volume: 25 start-page: 503 year: 2013 end-page: 526 ident: b0470 article-title: Metaheuristics: review and application publication-title: J. Exp. Theor. Artif. Intell. – volume: 11 start-page: 247 year: 2008 end-page: 257 ident: b0205 article-title: On kernel difference-weighted k-nearest neighbor classification publication-title: Pattern Anal. Appl. – volume: 6 start-page: 76728 year: 2018 end-page: 76740 ident: b0215 article-title: Image-based visibility estimation algorithm for intelligent transportation systems publication-title: IEEE Access – volume: 9 start-page: 1425 year: 2016 end-page: 1436 ident: b0625 article-title: SystemML: declarative machine learning on spark publication-title: Proc. VLDB Endow. – reference: M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv preprint arXiv:1603.04467, (2016). https://arxiv.org/abs1603.04467. – volume: 32 start-page: 340 year: 2017 end-page: 355 ident: b0500 publication-title: A Novel Hardware/ Software Partitioning Method Based on Position Disturbed Particle Swarm Optimization with Invasive Weed Optimization – start-page: 276 year: 2019 end-page: 281 ident: bib633 article-title: Clustering enabled classification using ensemble feature selection for intrusion detection publication-title: 2019 Int. Conf. Comput. Netw. Commun. ICNC – volume: 13 start-page: 47 year: 1996 end-page: 60 ident: b0345 article-title: The expectation-maximization algorithm publication-title: IEEE Signal Process. Mag. – volume: 17 start-page: 1235 year: 2016 end-page: 1241 ident: b0630 article-title: Mllib: machine learning in apache spark publication-title: J. Mach. Learn. Res. – reference: C.M. Bishop, Pattern Recognition and Machine Learning, 2006, Springer, ISBN: 978-0-387-31073-2. – volume: 18 start-page: 1 year: 2012 end-page: 52 ident: b0080 article-title: Hyperband: a novel bandit-based approach to hyperparameter optimization publication-title: J. Mach. Learn. Res. – reference: Q. Yao, et al., Taking Human out of Learning Applications: A Survey on Automated Machine Learning, arXiv preprint arXiv:1810.13306, (2018). http://arxiv.org/abs/1810.13306. – reference: M. Lindauer, K. Eggensperger, M. Feurer, S. Falkner, A. Biedenkapp, and F. Hutter, Smac v3: Algorithm configuration in python, 2017. https://github.com/automl/SMAC3. – reference: W. Yin, K. Kann, M. Yu, H. Schütze, Comparative Study of CNN and RNN for Natural Language Processing, arXiv preprint arXiv:1702.01923, (2017). https://arxiv.org/abs1702.01923. – volume: 201 start-page: 746 year: 2017 end-page: 755 ident: b0180 article-title: Comparing ridge and LASSO estimators for data analysis publication-title: Procedia Eng. – year: 2018 ident: bib631 article-title: DNS typo-squatting domain detection: a data analytics & machine learning based approach publication-title: 2018 IEEE Glob. Commun. Conf. GLOBECOM. – start-page: 1 year: 2018 end-page: 8 ident: b0475 article-title: Extending MLP ANN hyper-parameters Optimization by using Genetic Algorithm publication-title: Proc. Int. Jt. Conf. Neural Networks – reference: Y. Shi, R.C. Eberhart, Parameter Selection in Particle Swarm Optimization, Evolutionary Programming VII, Springer, 1998, pp. 591–600. – start-page: 1165 year: 2017 end-page: 1173 ident: b0580 article-title: Forward and reverse gradient-based hyperparameter optimization, 34th Int publication-title: Conf. Mach. Learn. ICML 2017 – volume: 3339 start-page: 488 year: 2004 end-page: 499 ident: b0245 article-title: Multinomial naive bayes for text categorization revisited publication-title: Lect. Notes Artif. Intell. (Subseries Lect. Notes Comput. Sci.) – start-page: 3460 year: 2015- (2015) end-page: 3468 ident: b0310 article-title: Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves publication-title: IJCAI Int. Jt. Conf. Artif. Intell. – volume: 200 start-page: 105992 year: 2020 ident: b0600 article-title: Systematic ensemble model selection approach for educational data mining publication-title: Knowl.-Based Syst. – reference: J.D.M. Rennie, L. Shih, J. Teevan, D.R. Karger Tackling the poor assumptions of Naive Bayes text classifiers, Proc. Twent. Int. Conf. Mach. Learn. ICML (2003), 616–623. – reference: M. Pumperla, Hyperas, 2019. http://maxpumperla.com/hyperas/. – volume: 9 start-page: 1 year: 2017 end-page: 13 ident: b0305 article-title: Deep-learning: Investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data publication-title: J. Cheminf. – volume: 53 start-page: 1605 year: 2007 end-page: 1614 ident: b0485 article-title: A novel population initialization method for accelerating evolutionary algorithms publication-title: Comput. Math. Appl. – reference: C. Witt, Worst-case and average-case approximations by simple randomized search heuristics, in: Proceedings of the 22nd Annual Symposium on Theoretical Aspects of Computer Science, STACS’05, Stuttgart, Germany, 2005, pp. 44-56. – start-page: 670 year: 2016 ident: 10.1016/j.neucom.2020.07.061_b0455 article-title: A new optimal sampling rule for multi-fidelity optimization via ordinal transformation publication-title: IEEE Int. Conf. Autom. Sci. Eng. – ident: 10.1016/j.neucom.2020.07.061_b0480 doi: 10.1109/CEC.2014.6900618 – volume: 8 year: 2015 ident: 10.1016/j.neucom.2020.07.061_b0530 article-title: Hyperopt: a Python library for model selection and hyperparameter optimization publication-title: Comput. Sci. Discov. doi: 10.1088/1749-4699/8/1/014008 – ident: 10.1016/j.neucom.2020.07.061_b0540 – volume: SMC-15 start-page: 580 year: 1985 ident: 10.1016/j.neucom.2020.07.061_b0200 article-title: A fuzzy K-nearest neighbor algorithm publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1985.6313426 – volume: 13 start-page: 47 issue: 6 year: 1996 ident: 10.1016/j.neucom.2020.07.061_b0345 article-title: The expectation-maximization algorithm publication-title: IEEE Signal Process. Mag. doi: 10.1109/79.543975 – ident: 10.1016/j.neucom.2020.07.061_b0560 – year: 2019 ident: 10.1016/j.neucom.2020.07.061_b0030 – volume: 5 start-page: 1 year: 2016 ident: 10.1016/j.neucom.2020.07.061_b0050 article-title: A review of automatic selection methods for machine learning algorithms and hyper-parameter values publication-title: Netw. Model. Anal. Heal. Inf. Bioinf. – year: 2013 ident: 10.1016/j.neucom.2020.07.061_b0440 – year: 2018 ident: 10.1016/j.neucom.2020.07.061_bib632 – start-page: 12 year: 2019 ident: 10.1016/j.neucom.2020.07.061_b0265 article-title: Machine learning for performance-aware virtual network function placement publication-title: 2019 IEEE Glob. Commun. Conf. GLOBECOM 2019 – Proc. – volume: 2 start-page: 1114 year: 2015 ident: 10.1016/j.neucom.2020.07.061_b0075 article-title: Efficient benchmarking of hyperparameter optimizers via surrogates publication-title: Proc. Natl. Conf. Artif. Intell. – start-page: 3460 year: 2015 ident: 10.1016/j.neucom.2020.07.061_b0310 article-title: Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves publication-title: IJCAI Int. Jt. Conf. Artif. Intell. – volume: 11 start-page: 2079 year: 2010 ident: 10.1016/j.neucom.2020.07.061_b0620 article-title: On over-fitting in model selection and subsequent selection bias in performance evaluation publication-title: J. Mach. Learn. Res. – volume: 58 start-page: 267 year: 1996 ident: 10.1016/j.neucom.2020.07.061_b0185 article-title: Regression shrinkage and selection via the Lasso publication-title: J. R. Stat. Soc. Ser. B doi: 10.1111/j.2517-6161.1996.tb02080.x – volume: 39 start-page: 277 year: 2006 ident: 10.1016/j.neucom.2020.07.061_b0385 article-title: Solving the small sample size problem in face recognition using generalized discriminant analysis publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2005.06.013 – ident: 10.1016/j.neucom.2020.07.061_b0595 – ident: 10.1016/j.neucom.2020.07.061_b0085 – volume: 95 start-page: 43 year: 2018 ident: 10.1016/j.neucom.2020.07.061_b0325 article-title: A new image classification method using CNN transfer learning and web data augmentation publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.11.028 – volume: 13 start-page: 281 issue: 1 year: 2012 ident: 10.1016/j.neucom.2020.07.061_b0065 article-title: Random search for hyper-parameter optimization publication-title: J. Mach. Learn. Res. – year: 2015 ident: 10.1016/j.neucom.2020.07.061_b0445 – volume: 25 start-page: 503 year: 2013 ident: 10.1016/j.neucom.2020.07.061_b0470 article-title: Metaheuristics: review and application publication-title: J. Exp. Theor. Artif. Intell. doi: 10.1080/0952813X.2013.782347 – start-page: 368 year: 2015 ident: 10.1016/j.neucom.2020.07.061_b0130 article-title: Automating model search for large scale machine learning publication-title: Proc. 6th ACM Symp. Cloud Comput. doi: 10.1145/2806777.2806945 – ident: 10.1016/j.neucom.2020.07.061_b0360 doi: 10.1109/ICADIWT.2014.6814687 – year: 2018 ident: 10.1016/j.neucom.2020.07.061_bib631 article-title: DNS typo-squatting domain detection: a data analytics & machine learning based approach publication-title: 2018 IEEE Glob. Commun. Conf. GLOBECOM. doi: 10.1109/GLOCOM.2018.8647679 – ident: 10.1016/j.neucom.2020.07.061_b0285 doi: 10.1109/ISEASP.2017.7976988 – volume: 74 start-page: 67 year: 2018 ident: 10.1016/j.neucom.2020.07.061_b0330 article-title: Genetic algorithms for hyperparameter optimization in predictive business process monitoring publication-title: Inf. Syst. doi: 10.1016/j.is.2018.01.003 – volume: 3339 start-page: 488 year: 2004 ident: 10.1016/j.neucom.2020.07.061_b0245 article-title: Multinomial naive bayes for text categorization revisited publication-title: Lect. Notes Artif. Intell. (Subseries Lect. Notes Comput. Sci.) – ident: 10.1016/j.neucom.2020.07.061_b0015 – start-page: 237 year: 2006 ident: 10.1016/j.neucom.2020.07.061_b0240 article-title: Comparing fuzzy Naive Bayes and Gaussian Naive Bayes for decision making in RoboCup 3D publication-title: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) – volume: 13 start-page: 2171 year: 2012 ident: 10.1016/j.neucom.2020.07.061_b0585 article-title: DEAP: evolutionary algorithms made easy publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.neucom.2020.07.061_b0235 doi: 10.1007/978-3-540-74958-5_35 – volume: 2000 start-page: 1 year: 1857 ident: 10.1016/j.neucom.2020.07.061_b0295 article-title: Ensemble methods in machine learning publication-title: Mult. Classif. Syst. – volume: 21 start-page: 660 year: 1991 ident: 10.1016/j.neucom.2020.07.061_b0260 article-title: A survey of decision tree classifier methodology publication-title: IEEE Trans. Syst. Man. Cybern. doi: 10.1109/21.97458 – ident: 10.1016/j.neucom.2020.07.061_b0225 – ident: 10.1016/j.neucom.2020.07.061_b0250 – start-page: 1 year: 2015 ident: 10.1016/j.neucom.2020.07.061_b0520 article-title: The impact of particles initialization in PSO: parameter estimation as a case in point publication-title: 2015 IEEE Conf. Comput. Intell. Bioinforma. Comput. Biol. CIBCB 2015 – ident: 10.1016/j.neucom.2020.07.061_b0055 – year: 2019 ident: 10.1016/j.neucom.2020.07.061_b0125 – start-page: 45 year: 2016 ident: 10.1016/j.neucom.2020.07.061_b0145 – ident: 10.1016/j.neucom.2020.07.061_b0565 – ident: 10.1016/j.neucom.2020.07.061_b0400 doi: 10.1007/978-3-540-31856-9_4 – ident: 10.1016/j.neucom.2020.07.061_b0135 – volume: 32 start-page: 340 year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0500 publication-title: A Novel Hardware/ Software Partitioning Method Based on Position Disturbed Particle Swarm Optimization with Invasive Weed Optimization – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.neucom.2020.07.061_b0150 article-title: Scikit-learn: machine learning in Python publication-title: J. Mach. Learn. Res. – start-page: 34 year: 2014 ident: 10.1016/j.neucom.2020.07.061_b0535 article-title: Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn publication-title: Proc. ICML Workshop AutoML – volume: 6 year: 2012 ident: 10.1016/j.neucom.2020.07.061_b0195 article-title: Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions publication-title: BMC Proc. BioMed Cent. – start-page: 1165 issue: 70 year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0580 article-title: Forward and reverse gradient-based hyperparameter optimization, 34th Int publication-title: Conf. Mach. Learn. ICML 2017 – volume: 200 start-page: 105992 year: 2020 ident: 10.1016/j.neucom.2020.07.061_b0600 article-title: Systematic ensemble model selection approach for educational data mining publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2020.105992 – year: 1995 ident: 10.1016/j.neucom.2020.07.061_b0605 – volume: 2 start-page: 387 year: 2006 ident: 10.1016/j.neucom.2020.07.061_b0380 article-title: Conditional linear discriminant analysis publication-title: Proc. – Int. Conf. Pattern Recognit. – ident: 10.1016/j.neucom.2020.07.061_b0010 – volume: 78 start-page: 225 year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0290 article-title: A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.02.017 – volume: 6 start-page: 76728 year: 2018 ident: 10.1016/j.neucom.2020.07.061_b0215 article-title: Image-based visibility estimation algorithm for intelligent transportation systems publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2884225 – volume: 10 start-page: 2137 issue: 8 year: 1998 ident: 10.1016/j.neucom.2020.07.061_b0410 article-title: Complexity issues in natural gradient descent method for training multilayer perceptrons publication-title: Neural Comput. doi: 10.1162/089976698300017007 – ident: 10.1016/j.neucom.2020.07.061_b0415 – ident: 10.1016/j.neucom.2020.07.061_b0280 doi: 10.1109/GLOCOM.2018.8647714 – start-page: 1 year: 2018 ident: 10.1016/j.neucom.2020.07.061_b0475 article-title: Extending MLP ANN hyper-parameters Optimization by using Genetic Algorithm publication-title: Proc. Int. Jt. Conf. Neural Networks – volume: 18 start-page: 1 year: 2012 ident: 10.1016/j.neucom.2020.07.061_b0080 article-title: Hyperband: a novel bandit-based approach to hyperparameter optimization publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.neucom.2020.07.061_b0115 – ident: 10.1016/j.neucom.2020.07.061_b0545 – start-page: 276 year: 2019 ident: 10.1016/j.neucom.2020.07.061_bib633 article-title: Clustering enabled classification using ensemble feature selection for intrusion detection publication-title: 2019 Int. Conf. Comput. Netw. Commun. ICNC doi: 10.1109/ICCNC.2019.8685636 – volume: 12 start-page: 939 year: 2018 ident: 10.1016/j.neucom.2020.07.061_b0320 article-title: Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition publication-title: IET Intell. Transp. Syst. doi: 10.1049/iet-its.2018.5127 – ident: 10.1016/j.neucom.2020.07.061_b0590 doi: 10.1007/978-3-030-05318-5_8 – volume: 25 start-page: 1097 year: 2012 ident: 10.1016/j.neucom.2020.07.061_b0610 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 9 start-page: 1967 year: 2012 ident: 10.1016/j.neucom.2020.07.061_b0365 article-title: Research on adaptive parameters determination in DBSCAN algorithm publication-title: J. Inf. Comput. Sci. – ident: 10.1016/j.neucom.2020.07.061_b0160 – start-page: 1 year: 2013 ident: 10.1016/j.neucom.2020.07.061_b0070 article-title: Towards an empirical foundation for assessing Bayesian optimization of hyperparameters publication-title: BayesOpt Work – volume: 61 start-page: 1 year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0120 article-title: An effective algorithm for hyperparameter optimization of neural networks publication-title: IBM J. Res. Dev. doi: 10.1147/JRD.2017.2709578 – volume: 12 start-page: 1889 issue: 8 year: 2000 ident: 10.1016/j.neucom.2020.07.061_b0405 article-title: Gradient-based optimization of hyperparameters publication-title: Neural Comput. doi: 10.1162/089976600300015187 – ident: 10.1016/j.neucom.2020.07.061_b0495 doi: 10.1007/BFb0040810 – ident: 10.1016/j.neucom.2020.07.061_b0435 – ident: 10.1016/j.neucom.2020.07.061_b0040 – ident: 10.1016/j.neucom.2020.07.061_b0615 – volume: 3056 start-page: 414 year: 2004 ident: 10.1016/j.neucom.2020.07.061_b0340 article-title: Cluster structure of K-means clustering via principal component analysis publication-title: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) – ident: 10.1016/j.neucom.2020.07.061_b0100 – ident: 10.1016/j.neucom.2020.07.061_b0255 – volume: 9 start-page: 1 year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0305 article-title: Deep-learning: Investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data publication-title: J. Cheminf. doi: 10.1186/s13321-017-0226-y – start-page: 151 year: 2000 ident: 10.1016/j.neucom.2020.07.061_b0490 article-title: Time complexity of genetic algorithms on exponentially scaled problems publication-title: Proc. Genet. Evol. Comput. Conf. – ident: 10.1016/j.neucom.2020.07.061_b0515 – volume: 34 start-page: 358 issue: 1 year: 2013 ident: 10.1016/j.neucom.2020.07.061_b0190 article-title: Applied logistic regression publication-title: Technometrics – ident: 10.1016/j.neucom.2020.07.061_b0570 – year: 2019 ident: 10.1016/j.neucom.2020.07.061_b0270 article-title: Tree-based intelligent intrusion detection system in internet of vehicles publication-title: 2019 IEEE Glob. Commun. Conf. GLOBECOM 2019 – Proc. doi: 10.1109/GLOBECOM38437.2019.9013892 – year: 1977 ident: 10.1016/j.neucom.2020.07.061_b0105 – volume: 34 start-page: 1 year: 2020 ident: 10.1016/j.neucom.2020.07.061_b0335 article-title: Student engagement level in e-learning environment: clustering using K-means publication-title: Am. J. Distance Educ. doi: 10.1080/08923647.2020.1696140 – ident: 10.1016/j.neucom.2020.07.061_b0300 – volume: 5 start-page: 1433 year: 2005 ident: 10.1016/j.neucom.2020.07.061_b0350 article-title: Fast and Robust gas identification system using an integrated gas sensor technology and Gaussian mixture models publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2005.858926 – ident: 10.1016/j.neucom.2020.07.061_b0370 – ident: 10.1016/j.neucom.2020.07.061_b0155 – volume: 12 start-page: 69 year: 1970 ident: 10.1016/j.neucom.2020.07.061_b0175 article-title: Ridge regression: applications to nonorthogonal problems publication-title: Technometrics doi: 10.1080/00401706.1970.10488635 – ident: 10.1016/j.neucom.2020.07.061_b0390 doi: 10.1609/aaai.v31i1.10647 – start-page: 2546 year: 2011 ident: 10.1016/j.neucom.2020.07.061_b0060 article-title: Algorithms for hyper-parameter optimization publication-title: Proc. Adv. Neural Inf. Process. Syst. – volume: 9 start-page: 155 year: 1997 ident: 10.1016/j.neucom.2020.07.061_b0210 article-title: Support vector regression machines publication-title: Adv. Neural Inf. Process. Syst. – volume: 61 start-page: 1 year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0025 article-title: An effective algorithm for hyperparameter optimization of neural networks publication-title: IBM J. Res. Dev. doi: 10.1147/JRD.2017.2709578 – year: 2013 ident: 10.1016/j.neucom.2020.07.061_b0020 – volume: 9 start-page: 1425 year: 2016 ident: 10.1016/j.neucom.2020.07.061_b0625 article-title: SystemML: declarative machine learning on spark publication-title: Proc. VLDB Endow. doi: 10.14778/3007263.3007279 – year: 2019 ident: 10.1016/j.neucom.2020.07.061_b0035 article-title: Effect of the sampling of a dataset in the hyperparameter optimization phase over the efficiency of a machine learning algorithm publication-title: Complexity doi: 10.1155/2019/6278908 – ident: 10.1016/j.neucom.2020.07.061_b0550 – year: 2020 ident: 10.1016/j.neucom.2020.07.061_bib638 publication-title: Hyperparameter Optimization of Machine Learning Algorithms – year: 2014 ident: 10.1016/j.neucom.2020.07.061_b0395 publication-title: Easy Hyperparameter Search Using Optunity, arXiv preprint arXiv:1412.1114 – ident: 10.1016/j.neucom.2020.07.061_b0355 doi: 10.1007/s10618-005-0361-3 – year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0420 – start-page: 1 year: 2019 ident: 10.1016/j.neucom.2020.07.061_b0165 publication-title: Optimization Models for Machine Learning: A Survey – year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0555 publication-title: GPflowOpt: A Bayesian Optimization Library using TensorFlow, arXiv preprint arXiv:1711.03845 – volume: 201 start-page: 746 year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0180 article-title: Comparing ridge and LASSO estimators for data analysis publication-title: Procedia Eng. doi: 10.1016/j.proeng.2017.09.615 – ident: 10.1016/j.neucom.2020.07.061_b0170 – volume: 11 start-page: 247 year: 2008 ident: 10.1016/j.neucom.2020.07.061_b0205 article-title: On kernel difference-weighted k-nearest neighbor classification publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-007-0100-z – ident: 10.1016/j.neucom.2020.07.061_b0045 – start-page: 2275 issue: 28 year: 2013 ident: 10.1016/j.neucom.2020.07.061_b0460 article-title: Almost optimal exploration in multi-armed bandits publication-title: 30th Int. Conf. Mach. Learn. ICML 2013 – volume: 2009 start-page: 332 issue: 5 year: 2009 ident: 10.1016/j.neucom.2020.07.061_b0510 article-title: A new population initialization method based on space transformation search, 5th Int publication-title: Conf. Nat. Comput. ICNC – ident: 10.1016/j.neucom.2020.07.061_b0090 – ident: 10.1016/j.neucom.2020.07.061_b0275 doi: 10.1109/ICDM.2017.137 – volume: 9 year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0315 article-title: Effective hyperparameter optimization using Nelder-Mead method in deep learning publication-title: IPSJ Trans. Comput. Vis. Appl. – volume: 53 start-page: 217 issue: 2 year: 2011 ident: 10.1016/j.neucom.2020.07.061_b0375 article-title: Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions publication-title: SIAM Rev. doi: 10.1137/090771806 – volume: 15 start-page: 3735 year: 2015 ident: 10.1016/j.neucom.2020.07.061_b0525 article-title: BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits publication-title: J. Mach. Learn. Res. – volume: 5 start-page: 507 year: 2011 ident: 10.1016/j.neucom.2020.07.061_b0430 article-title: Sequential model-based optimization for general algorithm configuration publication-title: Proc. LION – year: 2020 ident: 10.1016/j.neucom.2020.07.061_bib637 article-title: Multi-split optimized bagging ensemble model selection for multi-class educational data mining publication-title: Springer’s Appl. Intell. doi: 10.1007/s10489-020-01776-3 – start-page: 2323 issue: 4 year: 2018 ident: 10.1016/j.neucom.2020.07.061_b0465 article-title: BOHB: robust and efficient hyperparameter optimization at scale publication-title: 35th Int. Conf. Mach. Learn. ICML 2018 – volume: 53 start-page: 1605 year: 2007 ident: 10.1016/j.neucom.2020.07.061_b0485 article-title: A novel population initialization method for accelerating evolutionary algorithms publication-title: Comput. Math. Appl. doi: 10.1016/j.camwa.2006.07.013 – volume: 14 start-page: 69 year: 2004 ident: 10.1016/j.neucom.2020.07.061_b0425 article-title: Gaussian processes for machine learning publication-title: Int. J. Neural Syst. doi: 10.1142/S0129065704001899 – volume: 148 start-page: 161 year: 2006 ident: 10.1016/j.neucom.2020.07.061_b0140 article-title: An empirical comparison of supervised learning algorithms publication-title: ACM Int. Conf. Proc. Ser. – volume: 2 start-page: 888 year: 2003 ident: 10.1016/j.neucom.2020.07.061_b0220 article-title: Modified logistic regression: an approximation to SVM and its applications in large-scale text categorization publication-title: Proceedings Twent. Int. Conf. Mach. Learn. – ident: 10.1016/j.neucom.2020.07.061_b0575 doi: 10.21105/joss.00034 – year: 2020 ident: 10.1016/j.neucom.2020.07.061_bib634 article-title: Ensemble-based feature selection and classification model for DNS typo-squatting detection publication-title: 2020 IEEE Can. Conf. Electr. Comput. Eng. doi: 10.1109/CCECE47787.2020.9255697 – start-page: 481 year: 2017 ident: 10.1016/j.neucom.2020.07.061_b0095 article-title: Particle swarm optimization for hyper-parameter selection in deep neural networks publication-title: Proc. ACM Int. Conf. Genet. Evol. Comput. doi: 10.1145/3071178.3071208 – start-page: 41 year: 2001 ident: 10.1016/j.neucom.2020.07.061_b0230 article-title: An empirical study of the naive Bayes classifier publication-title: IJCAI 2001 Work Empir. Methods Artif. Intell. – volume: 144 start-page: 1 year: 2018 ident: 10.1016/j.neucom.2020.07.061_b0505 article-title: Multiobjective dynamic-guiding PSO for optimizing work shift schedules publication-title: J. Constr. Eng. Manag. doi: 10.1061/(ASCE)CO.1943-7862.0001548 – volume: 17 start-page: 1235 issue: 1 year: 2016 ident: 10.1016/j.neucom.2020.07.061_b0630 article-title: Mllib: machine learning in apache spark publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.neucom.2020.07.061_b0450 doi: 10.1007/978-3-7908-2604-3_16 – volume: 349 start-page: 255 year: 2015 ident: 10.1016/j.neucom.2020.07.061_b0005 article-title: Machine learning: trends, perspectives, and prospects publication-title: Science doi: 10.1126/science.aaa8415 – volume: 8 start-page: 231 year: 2015 ident: 10.1016/j.neucom.2020.07.061_b0110 article-title: Convex optimization: algorithms and complexity publication-title: Found. Trends Mach. Learn. doi: 10.1561/2200000050
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Snippet	Machine learning algorithms have been used widely in various applications and areas. To fit a machine learning model into different problems, its...
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SubjectTerms	Bayesian optimization Genetic algorithm Grid search Hyper-parameter optimization Machine learning Particle swarm optimization
Title	On hyperparameter optimization of machine learning algorithms: Theory and practice
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