Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis
Support vector machine (SVM) is a widely used pattern classification method that its classification accuracy is greatly influenced by both kernel parameter setting and feature selection. Therefore, in this study, to perform parameter optimization and feature selection simultaneously for SVM, we prop...
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| Published in | Applied soft computing Vol. 88; p. 105946 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.03.2020
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Support vector machine (SVM) is a widely used pattern classification method that its classification accuracy is greatly influenced by both kernel parameter setting and feature selection. Therefore, in this study, to perform parameter optimization and feature selection simultaneously for SVM, we propose an improved whale optimization algorithm (CMWOA), which combines chaotic and multi-swarm strategies. Using several well-known medical diagnosis problems of breast cancer, diabetes, and erythemato-squamous, the proposed SVM model, termed CMWOAFS-SVM, was compared with multiple competitive SVM models based on other optimization algorithms including the original algorithm, particle swarm optimization, bacterial foraging optimization, and genetic algorithms. The experimental results demonstrate that CMWOAFS-SVM significantly outperformed all the other competitors in terms of classification performance and feature subset size.
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•A multi-swarms with stratified mechanism is introduced to construct a multi-swarms WOA.•Chaos population initialization and self-adaption chaotic disturbance mechanism are introduced.•The proposed method has tackled the parameter optimization and feature selection. |
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| AbstractList | Support vector machine (SVM) is a widely used pattern classification method that its classification accuracy is greatly influenced by both kernel parameter setting and feature selection. Therefore, in this study, to perform parameter optimization and feature selection simultaneously for SVM, we propose an improved whale optimization algorithm (CMWOA), which combines chaotic and multi-swarm strategies. Using several well-known medical diagnosis problems of breast cancer, diabetes, and erythemato-squamous, the proposed SVM model, termed CMWOAFS-SVM, was compared with multiple competitive SVM models based on other optimization algorithms including the original algorithm, particle swarm optimization, bacterial foraging optimization, and genetic algorithms. The experimental results demonstrate that CMWOAFS-SVM significantly outperformed all the other competitors in terms of classification performance and feature subset size.
[Display omitted]
•A multi-swarms with stratified mechanism is introduced to construct a multi-swarms WOA.•Chaos population initialization and self-adaption chaotic disturbance mechanism are introduced.•The proposed method has tackled the parameter optimization and feature selection. |
| ArticleNumber | 105946 |
| Author | Chen, Huiling Wang, Mingjing |
| Author_xml | – sequence: 1 givenname: Mingjing surname: Wang fullname: Wang, Mingjing email: mingjingwang@duytan.edu.vn organization: Hangzhou Medical College, Hangzhou 310053, Zhejiang, China – sequence: 2 givenname: Huiling orcidid: 0000-0002-7714-9693 surname: Chen fullname: Chen, Huiling email: chenhuiling.jlu@gmail.com organization: College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China |
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| Cites_doi | 10.1016/j.eswa.2019.03.043 10.1016/j.eswa.2011.01.120 10.1155/2017/9512741 10.1016/j.knosys.2016.01.002 10.1111/j.1468-0394.2007.00418.x 10.1016/j.eswa.2014.01.021 10.1109/ACCESS.2017.2741521 10.1080/00207721.2013.801096 10.1016/j.apm.2019.03.046 10.1109/TSMC.2016.2569474 10.1016/j.ins.2014.02.123 10.1007/s10916-018-1055-x 10.1016/j.eswa.2004.08.009 10.1016/j.apm.2019.02.004 10.1007/s10489-007-0073-z 10.1186/s12859-019-2771-z 10.1016/j.camwa.2006.07.013 10.1016/j.eswa.2007.02.002 10.1016/j.advengsoft.2016.01.008 10.1016/j.eswa.2019.113018 10.1109/ACCESS.2018.2876996 10.1038/s41598-017-10890-1 10.1016/j.compbiolchem.2018.11.017 10.1016/j.compbiomed.2015.02.003 10.1016/j.neucom.2017.04.060 10.1007/s00521-019-04015-0 10.1016/j.eswa.2011.03.066 10.1023/A:1012487302797 10.1162/089976603321891855 10.1145/1961189.1961199 10.1109/ACCESS.2019.2902306 10.1007/s10916-011-9723-0 10.1145/130385.130401 10.1016/j.eswa.2005.09.024 10.1080/15397734.2016.1213639 10.1093/brain/awm319 10.1016/j.neucom.2017.04.053 10.1016/j.apenergy.2017.05.029 |
| ContentType | Journal Article |
| Copyright | 2019 Elsevier B.V. |
| Copyright_xml | – notice: 2019 Elsevier B.V. |
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| References | Kaveh, Ghazaan (b31) 2017; 45 Chen (b18) 2019 Shen (b49) 2016; 96 Hu (b54) 2015; 59 Chen (b3) 2011; 38 Liu (b47) 2015; 46 Fu (b14) 2014 Xu (b44) 2019; 129 Huang (b17) 2019; 20 Gold, Sollich (b11) 2002; 55 Saremi, Mirjalili, Lewis (b39) 2014 Heidari (b24) 2019 Elaziz (b28) 2017; 7 B.E. Boser, A training algorithm for optimal margin classifiers, in: ACM Fifth Workshop on Computational Lerning Theory, Pittsburgh, 1992. Chen (b19) 2011; 38 Wang (b34) 2017; 267 Chen (b40) 2019 Zhang (b42) 2019; 7 Chen (b13) 2014; 239 Huang, Wang (b16) 2006; 31 Chen (b22) 2019; 71 Yu (b43) 2019 Klöppel (b52) 2008; 131 Oliva, Aziz, Hassanien (b27) 2017; 200 Harikarthik, Palanisamy, Ramanathan (b29) 2017 Guyon (b51) 2002; 46 Hassan, Hassanien (b30) 2017 Zhang (b45) 2018; 6 Shin, Lee, Kim (b6) 2005; 28 Mirjalili, Lewis (b21) 2016; 95 Rahnamayan, Tizhoosh, Salama (b36) 2007; 53 Zhao (b41) 2019; 78 Frieb, Harrison (b5) 1998 Chen (b23) 2019 Maglogiannis, Zafiropoulos, Anagnostopoulos (b8) 2009; 30 Luo (b25) 2019; 73 B. Scholkopf, C.J.C. Burges, A.J. Smola, Advances in Kernel Methods - Support Vector Learning. 174 (4) (1998) 1097-105. Li (b20) 2017; 2017 Mafarja, Mirjalili (b32) 2017; 260 Wang (b46) 2014; 274 Chen (b7) 2012; 36 Huang, Huang, Wang (b38) 2017; 47 Karthikeyan, Alli (b48) 2018; 42 Chen (b4) 2011; 38 Vapnik, Vladimir (b2) 2002; 8 Cai, Gu, Chen (b15) 2017; 5 Chen (b50) 2014; 239 Wu (b33) 2017 Chang, Lin (b12) 2011; 2 Aljarah, Faris, Mirjalili (b26) 2016 El-Dahshan (b53) 2014; 41 Keerthi, Lin (b10) 2003; 15 Uuml, Beyli (b9) 2007; 24 Coelho, Mariani (b37) 2008; 34 Chen (10.1016/j.asoc.2019.105946_b3) 2011; 38 Huang (10.1016/j.asoc.2019.105946_b16) 2006; 31 Rahnamayan (10.1016/j.asoc.2019.105946_b36) 2007; 53 Mirjalili (10.1016/j.asoc.2019.105946_b21) 2016; 95 Huang (10.1016/j.asoc.2019.105946_b17) 2019; 20 Gold (10.1016/j.asoc.2019.105946_b11) 2002; 55 Xu (10.1016/j.asoc.2019.105946_b44) 2019; 129 Cai (10.1016/j.asoc.2019.105946_b15) 2017; 5 Wang (10.1016/j.asoc.2019.105946_b46) 2014; 274 Fu (10.1016/j.asoc.2019.105946_b14) 2014 Chen (10.1016/j.asoc.2019.105946_b23) 2019 Maglogiannis (10.1016/j.asoc.2019.105946_b8) 2009; 30 Kaveh (10.1016/j.asoc.2019.105946_b31) 2017; 45 Shen (10.1016/j.asoc.2019.105946_b49) 2016; 96 Frieb (10.1016/j.asoc.2019.105946_b5) 1998 Shin (10.1016/j.asoc.2019.105946_b6) 2005; 28 Klöppel (10.1016/j.asoc.2019.105946_b52) 2008; 131 Vapnik (10.1016/j.asoc.2019.105946_b2) 2002; 8 Karthikeyan (10.1016/j.asoc.2019.105946_b48) 2018; 42 Elaziz (10.1016/j.asoc.2019.105946_b28) 2017; 7 10.1016/j.asoc.2019.105946_b1 Chang (10.1016/j.asoc.2019.105946_b12) 2011; 2 10.1016/j.asoc.2019.105946_b35 El-Dahshan (10.1016/j.asoc.2019.105946_b53) 2014; 41 Oliva (10.1016/j.asoc.2019.105946_b27) 2017; 200 Huang (10.1016/j.asoc.2019.105946_b38) 2017; 47 Yu (10.1016/j.asoc.2019.105946_b43) 2019 Keerthi (10.1016/j.asoc.2019.105946_b10) 2003; 15 Zhang (10.1016/j.asoc.2019.105946_b42) 2019; 7 Chen (10.1016/j.asoc.2019.105946_b19) 2011; 38 Chen (10.1016/j.asoc.2019.105946_b18) 2019 Chen (10.1016/j.asoc.2019.105946_b40) 2019 Luo (10.1016/j.asoc.2019.105946_b25) 2019; 73 Harikarthik (10.1016/j.asoc.2019.105946_b29) 2017 Saremi (10.1016/j.asoc.2019.105946_b39) 2014 Chen (10.1016/j.asoc.2019.105946_b7) 2012; 36 Chen (10.1016/j.asoc.2019.105946_b50) 2014; 239 Guyon (10.1016/j.asoc.2019.105946_b51) 2002; 46 Wang (10.1016/j.asoc.2019.105946_b34) 2017; 267 Mafarja (10.1016/j.asoc.2019.105946_b32) 2017; 260 Zhao (10.1016/j.asoc.2019.105946_b41) 2019; 78 Chen (10.1016/j.asoc.2019.105946_b4) 2011; 38 Uuml (10.1016/j.asoc.2019.105946_b9) 2007; 24 Heidari (10.1016/j.asoc.2019.105946_b24) 2019 Chen (10.1016/j.asoc.2019.105946_b22) 2019; 71 Aljarah (10.1016/j.asoc.2019.105946_b26) 2016 Zhang (10.1016/j.asoc.2019.105946_b45) 2018; 6 Coelho (10.1016/j.asoc.2019.105946_b37) 2008; 34 Hassan (10.1016/j.asoc.2019.105946_b30) 2017 Chen (10.1016/j.asoc.2019.105946_b13) 2014; 239 Li (10.1016/j.asoc.2019.105946_b20) 2017; 2017 Hu (10.1016/j.asoc.2019.105946_b54) 2015; 59 Wu (10.1016/j.asoc.2019.105946_b33) 2017 Liu (10.1016/j.asoc.2019.105946_b47) 2015; 46 |
| References_xml | – volume: 15 start-page: 1667 year: 2003 ident: b10 article-title: Asymptotic behaviors of support vector machines with Gaussian kernel publication-title: Neural Comput. – volume: 20 start-page: 290 year: 2019 ident: b17 article-title: A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features publication-title: BMC Bioinformatics – volume: 131 start-page: 681 year: 2008 end-page: 689 ident: b52 article-title: Automatic classification of MR scans in Alzheimer’s disease publication-title: Brain – volume: 129 start-page: 135 year: 2019 end-page: 155 ident: b44 article-title: An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks publication-title: Expert Syst. Appl. – volume: 47 start-page: 1229 year: 2017 end-page: 1240 ident: b38 article-title: Chaos and bifurcation control of torque-stiffness-controlled dynamic bipedal walking publication-title: IEEE Trans. Syst. Man Cybern. Syst. – volume: 8 year: 2002 ident: b2 article-title: The nature of statistical learning theory publication-title: IEEE Trans. Neural Netw. – year: 2017 ident: b33 article-title: Path planning for solar-powered UAV in urban environment publication-title: Neurocomputing – year: 2019 ident: b40 article-title: Parameters identification of photovoltaic cells and modules using diversification-enriched harris hawks optimization with chaotic drifts publication-title: J. Cleaner Prod. – volume: 239 start-page: 180 year: 2014 end-page: 197 ident: b13 article-title: Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy publication-title: Appl. Math. Comput. – volume: 7 year: 2017 ident: b28 article-title: Analysis of bioactive amino acids from fish hydrolysates with a new bioinformatic intelligent system approach publication-title: Sci. Rep. – reference: B.E. Boser, A training algorithm for optimal margin classifiers, in: ACM Fifth Workshop on Computational Lerning Theory, Pittsburgh, 1992. – start-page: 1 year: 2017 end-page: 10 ident: b29 article-title: Optimal test suite selection in regression testing with testcase prioritization using modified Ann and Whale optimization algorithm publication-title: Cluster Comput. – year: 2019 ident: b43 article-title: Chaos-enhanced synchronized bat optimizer publication-title: Appl. Math. Model. – year: 1998 ident: b5 article-title: Support vector neural networks – volume: 38 start-page: 9014 year: 2011 end-page: 9022 ident: b4 article-title: A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis publication-title: Expert Syst. Appl. – volume: 267 start-page: 69 year: 2017 end-page: 84 ident: b34 article-title: Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses publication-title: Neurocomputing – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: b21 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. – year: 2019 ident: b24 article-title: An enhanced associative learning-based exploratory whale optimizer for global optimization publication-title: Neural Comput. Appl. – volume: 46 start-page: 919 year: 2015 end-page: 931 ident: b47 article-title: A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection publication-title: Internat. J. Systems Sci. – volume: 55 start-page: 221 year: 2002 end-page: 249 ident: b11 article-title: Model selection for support vector machine classification publication-title: Neurocomputing – volume: 45 start-page: 345 year: 2017 end-page: 362 ident: b31 article-title: Enhanced whale optimization algorithm for sizing optimization of skeletal structures publication-title: Mech. Based Des. Struct. Mach. – volume: 200 start-page: 141 year: 2017 end-page: 154 ident: b27 article-title: Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm publication-title: Appl. Energy – volume: 5 start-page: 17188 year: 2017 end-page: 17200 ident: b15 article-title: A new hybrid intelligent framework for predicting parkinson’s disease publication-title: IEEE Access – volume: 6 start-page: 64905 year: 2018 end-page: 64919 ident: b45 article-title: Chaos enhanced bacterial foraging optimization for global optimization publication-title: IEEE Access – volume: 96 start-page: 61 year: 2016 end-page: 75 ident: b49 article-title: Evolving support vector machines using fruit fly optimization for medical data classification publication-title: Knowl.-Based Syst. – volume: 260 start-page: 302 year: 2017 end-page: 312 ident: b32 article-title: Hybrid whale optimization algorithm with simulated annealing for feature selection publication-title: Neurocomputing – reference: B. Scholkopf, C.J.C. Burges, A.J. Smola, Advances in Kernel Methods - Support Vector Learning. 174 (4) (1998) 1097-105. – volume: 34 start-page: 1905 year: 2008 end-page: 1913 ident: b37 article-title: Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization publication-title: Expert Syst. Appl. – start-page: 1 year: 2017 end-page: 8 ident: b30 article-title: Retinal fundus vasculature multilevel segmentation using whale optimization algorithm publication-title: Signal Image Video Process. – volume: 31 start-page: 231 year: 2006 end-page: 240 ident: b16 article-title: A GA-based feature selection and parameters optimizationfor support vector machines publication-title: Expert Syst. Appl. – volume: 274 start-page: 17 year: 2014 end-page: 34 ident: b46 article-title: Chaotic krill herd algorithm publication-title: Inform. Sci. – volume: 38 start-page: 11796 year: 2011 end-page: 11803 ident: b3 article-title: A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis publication-title: Expert Syst. Appl. – volume: 71 start-page: 45 year: 2019 end-page: 59 ident: b22 article-title: A balanced whale optimization algorithm for constrained engineering design problems publication-title: Appl. Math. Model. – year: 2019 ident: b23 article-title: An efficient double adaptive random spare reinforced whale optimization algorithm publication-title: Expert Syst. Appl. – volume: 53 start-page: 1605 year: 2007 end-page: 1614 ident: b36 article-title: A novel population initialization method for accelerating evolutionary algorithms publication-title: Comput. Math. Appl. – volume: 2017 start-page: 15 year: 2017 ident: b20 article-title: An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis publication-title: Comput. Math. Methods Med. – volume: 41 start-page: 5526 year: 2014 end-page: 5545 ident: b53 article-title: Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm publication-title: Expert Syst. Appl. – volume: 24 start-page: 17 year: 2007 end-page: 31 ident: b9 article-title: Comparison of different classification algorithms in clinical decision-making publication-title: Expert Syst. – volume: 2 start-page: 1 year: 2011 end-page: 27 ident: b12 article-title: LIBSVM: A library for support vector machines publication-title: ACM Trans. Intell. Syst. Technol. – volume: 73 start-page: 109 year: 2019 end-page: 123 ident: b25 article-title: Multi-strategy boosted mutative whale-inspired optimization approaches publication-title: Appl. Math. Model. – start-page: 42 year: 2014 end-page: 49 ident: b14 article-title: A New Evolutionary Support Vector Machine with Application To Parkinson’s Disease Diagnosis – volume: 239 start-page: 180 year: 2014 end-page: 197 ident: b50 article-title: Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy publication-title: Appl. Math. Comput. – volume: 46 start-page: 389 year: 2002 end-page: 422 ident: b51 article-title: Gene selection for cancer classification using support vector machines publication-title: Mach. Learn. – start-page: 1077 year: 2014 end-page: 1097 ident: b39 article-title: Biogeography-Based Optimisation with Chaos – volume: 42 year: 2018 ident: b48 article-title: Feature selection and parameters optimization of support vector machines based on hybrid glowworm swarm optimization for classification of diabetic retinopathy publication-title: J. Med. Syst. – volume: 78 start-page: 481 year: 2019 end-page: 490 ident: b41 article-title: Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients publication-title: Comput. Biol. Chem. – volume: 30 start-page: 24 year: 2009 end-page: 36 ident: b8 article-title: An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers publication-title: Appl. Intell. – start-page: 1 year: 2016 end-page: 15 ident: b26 article-title: Optimizing connection weights in neural networks using the whale optimization algorithm publication-title: Soft Comput. – volume: 38 start-page: 9014 year: 2011 end-page: 9022 ident: b19 article-title: A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis publication-title: Expert Syst. Appl. – volume: 59 start-page: 116 year: 2015 end-page: 124 ident: b54 article-title: An efficient machine learning approach for diagnosis of paraquat-poisoned patients publication-title: Comput. Biol. Med. – year: 2019 ident: b18 article-title: Efficient multi-population outpost fruit fly-driven optimizers: Framework and advances in support vector machines publication-title: Expert Syst. Appl. – volume: 28 start-page: 127 year: 2005 end-page: 135 ident: b6 article-title: An application of support vector machines in bankruptcy prediction model publication-title: Expert Syst. Appl. – volume: 36 start-page: 2505 year: 2012 end-page: 2519 ident: b7 article-title: Support vector machine based diagnostic system for breast Cancer using swarm intelligence publication-title: J. Med. Syst. – volume: 7 start-page: 31243 year: 2019 end-page: 31261 ident: b42 article-title: Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers publication-title: IEEE Access – year: 2019 ident: 10.1016/j.asoc.2019.105946_b18 article-title: Efficient multi-population outpost fruit fly-driven optimizers: Framework and advances in support vector machines publication-title: Expert Syst. Appl. – volume: 129 start-page: 135 year: 2019 ident: 10.1016/j.asoc.2019.105946_b44 article-title: An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.03.043 – volume: 8 issue: 6 year: 2002 ident: 10.1016/j.asoc.2019.105946_b2 article-title: The nature of statistical learning theory publication-title: IEEE Trans. Neural Netw. – volume: 38 start-page: 9014 issue: 7 year: 2011 ident: 10.1016/j.asoc.2019.105946_b4 article-title: A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.01.120 – volume: 2017 start-page: 15 year: 2017 ident: 10.1016/j.asoc.2019.105946_b20 article-title: An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis publication-title: Comput. Math. Methods Med. doi: 10.1155/2017/9512741 – year: 2017 ident: 10.1016/j.asoc.2019.105946_b33 article-title: Path planning for solar-powered UAV in urban environment publication-title: Neurocomputing – volume: 96 start-page: 61 year: 2016 ident: 10.1016/j.asoc.2019.105946_b49 article-title: Evolving support vector machines using fruit fly optimization for medical data classification publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2016.01.002 – volume: 24 start-page: 17 issue: 1 year: 2007 ident: 10.1016/j.asoc.2019.105946_b9 article-title: Comparison of different classification algorithms in clinical decision-making publication-title: Expert Syst. doi: 10.1111/j.1468-0394.2007.00418.x – start-page: 1 year: 2016 ident: 10.1016/j.asoc.2019.105946_b26 article-title: Optimizing connection weights in neural networks using the whale optimization algorithm publication-title: Soft Comput. – volume: 41 start-page: 5526 issue: 11 year: 2014 ident: 10.1016/j.asoc.2019.105946_b53 article-title: Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.01.021 – start-page: 1077 year: 2014 ident: 10.1016/j.asoc.2019.105946_b39 – year: 1998 ident: 10.1016/j.asoc.2019.105946_b5 – volume: 55 start-page: 221 issue: 1 year: 2002 ident: 10.1016/j.asoc.2019.105946_b11 article-title: Model selection for support vector machine classification publication-title: Neurocomputing – volume: 5 start-page: 17188 year: 2017 ident: 10.1016/j.asoc.2019.105946_b15 article-title: A new hybrid intelligent framework for predicting parkinson’s disease publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2741521 – volume: 46 start-page: 919 issue: 5 year: 2015 ident: 10.1016/j.asoc.2019.105946_b47 article-title: A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection publication-title: Internat. J. Systems Sci. doi: 10.1080/00207721.2013.801096 – volume: 73 start-page: 109 year: 2019 ident: 10.1016/j.asoc.2019.105946_b25 article-title: Multi-strategy boosted mutative whale-inspired optimization approaches publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2019.03.046 – volume: 47 start-page: 1229 issue: 7 year: 2017 ident: 10.1016/j.asoc.2019.105946_b38 article-title: Chaos and bifurcation control of torque-stiffness-controlled dynamic bipedal walking publication-title: IEEE Trans. Syst. Man Cybern. Syst. doi: 10.1109/TSMC.2016.2569474 – start-page: 1 year: 2017 ident: 10.1016/j.asoc.2019.105946_b30 article-title: Retinal fundus vasculature multilevel segmentation using whale optimization algorithm publication-title: Signal Image Video Process. – volume: 274 start-page: 17 year: 2014 ident: 10.1016/j.asoc.2019.105946_b46 article-title: Chaotic krill herd algorithm publication-title: Inform. Sci. doi: 10.1016/j.ins.2014.02.123 – volume: 42 issue: 10 year: 2018 ident: 10.1016/j.asoc.2019.105946_b48 article-title: Feature selection and parameters optimization of support vector machines based on hybrid glowworm swarm optimization for classification of diabetic retinopathy publication-title: J. Med. Syst. doi: 10.1007/s10916-018-1055-x – volume: 28 start-page: 127 issue: 1 year: 2005 ident: 10.1016/j.asoc.2019.105946_b6 article-title: An application of support vector machines in bankruptcy prediction model publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2004.08.009 – volume: 239 start-page: 180 issue: 8 year: 2014 ident: 10.1016/j.asoc.2019.105946_b13 article-title: Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy publication-title: Appl. Math. Comput. – volume: 71 start-page: 45 year: 2019 ident: 10.1016/j.asoc.2019.105946_b22 article-title: A balanced whale optimization algorithm for constrained engineering design problems publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2019.02.004 – start-page: 42 year: 2014 ident: 10.1016/j.asoc.2019.105946_b14 – volume: 30 start-page: 24 issue: 1 year: 2009 ident: 10.1016/j.asoc.2019.105946_b8 article-title: An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers publication-title: Appl. Intell. doi: 10.1007/s10489-007-0073-z – volume: 20 start-page: 290 issue: 8 year: 2019 ident: 10.1016/j.asoc.2019.105946_b17 article-title: A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features publication-title: BMC Bioinformatics doi: 10.1186/s12859-019-2771-z – volume: 53 start-page: 1605 issue: 10 year: 2007 ident: 10.1016/j.asoc.2019.105946_b36 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: 34 start-page: 1905 issue: 3 year: 2008 ident: 10.1016/j.asoc.2019.105946_b37 article-title: Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2007.02.002 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.asoc.2019.105946_b21 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.008 – year: 2019 ident: 10.1016/j.asoc.2019.105946_b23 article-title: An efficient double adaptive random spare reinforced whale optimization algorithm publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.113018 – volume: 6 start-page: 64905 year: 2018 ident: 10.1016/j.asoc.2019.105946_b45 article-title: Chaos enhanced bacterial foraging optimization for global optimization publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2876996 – volume: 7 issue: 1 year: 2017 ident: 10.1016/j.asoc.2019.105946_b28 article-title: Analysis of bioactive amino acids from fish hydrolysates with a new bioinformatic intelligent system approach publication-title: Sci. Rep. doi: 10.1038/s41598-017-10890-1 – volume: 78 start-page: 481 year: 2019 ident: 10.1016/j.asoc.2019.105946_b41 article-title: Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2018.11.017 – ident: 10.1016/j.asoc.2019.105946_b35 – volume: 59 start-page: 116 issue: C year: 2015 ident: 10.1016/j.asoc.2019.105946_b54 article-title: An efficient machine learning approach for diagnosis of paraquat-poisoned patients publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2015.02.003 – year: 2019 ident: 10.1016/j.asoc.2019.105946_b40 article-title: Parameters identification of photovoltaic cells and modules using diversification-enriched harris hawks optimization with chaotic drifts publication-title: J. Cleaner Prod. – volume: 267 start-page: 69 year: 2017 ident: 10.1016/j.asoc.2019.105946_b34 article-title: Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.04.060 – year: 2019 ident: 10.1016/j.asoc.2019.105946_b43 article-title: Chaos-enhanced synchronized bat optimizer publication-title: Appl. Math. Model. – year: 2019 ident: 10.1016/j.asoc.2019.105946_b24 article-title: An enhanced associative learning-based exploratory whale optimizer for global optimization publication-title: Neural Comput. Appl. doi: 10.1007/s00521-019-04015-0 – volume: 38 start-page: 11796 issue: 9 year: 2011 ident: 10.1016/j.asoc.2019.105946_b3 article-title: A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.03.066 – volume: 46 start-page: 389 issue: 1–3 year: 2002 ident: 10.1016/j.asoc.2019.105946_b51 article-title: Gene selection for cancer classification using support vector machines publication-title: Mach. Learn. doi: 10.1023/A:1012487302797 – volume: 15 start-page: 1667 issue: 7 year: 2003 ident: 10.1016/j.asoc.2019.105946_b10 article-title: Asymptotic behaviors of support vector machines with Gaussian kernel publication-title: Neural Comput. doi: 10.1162/089976603321891855 – volume: 2 start-page: 1 issue: 3 year: 2011 ident: 10.1016/j.asoc.2019.105946_b12 article-title: LIBSVM: A library for support vector machines publication-title: ACM Trans. Intell. Syst. Technol. doi: 10.1145/1961189.1961199 – start-page: 1 year: 2017 ident: 10.1016/j.asoc.2019.105946_b29 article-title: Optimal test suite selection in regression testing with testcase prioritization using modified Ann and Whale optimization algorithm publication-title: Cluster Comput. – volume: 7 start-page: 31243 year: 2019 ident: 10.1016/j.asoc.2019.105946_b42 article-title: Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2902306 – volume: 239 start-page: 180 year: 2014 ident: 10.1016/j.asoc.2019.105946_b50 article-title: Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy publication-title: Appl. Math. Comput. – volume: 36 start-page: 2505 issue: 4 year: 2012 ident: 10.1016/j.asoc.2019.105946_b7 article-title: Support vector machine based diagnostic system for breast Cancer using swarm intelligence publication-title: J. Med. Syst. doi: 10.1007/s10916-011-9723-0 – volume: 38 start-page: 9014 issue: 7 year: 2011 ident: 10.1016/j.asoc.2019.105946_b19 article-title: A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.01.120 – ident: 10.1016/j.asoc.2019.105946_b1 doi: 10.1145/130385.130401 – volume: 31 start-page: 231 issue: 2 year: 2006 ident: 10.1016/j.asoc.2019.105946_b16 article-title: A GA-based feature selection and parameters optimizationfor support vector machines publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2005.09.024 – volume: 45 start-page: 345 issue: 3 year: 2017 ident: 10.1016/j.asoc.2019.105946_b31 article-title: Enhanced whale optimization algorithm for sizing optimization of skeletal structures publication-title: Mech. Based Des. Struct. Mach. doi: 10.1080/15397734.2016.1213639 – volume: 131 start-page: 681 issue: 3 year: 2008 ident: 10.1016/j.asoc.2019.105946_b52 article-title: Automatic classification of MR scans in Alzheimer’s disease publication-title: Brain doi: 10.1093/brain/awm319 – volume: 260 start-page: 302 year: 2017 ident: 10.1016/j.asoc.2019.105946_b32 article-title: Hybrid whale optimization algorithm with simulated annealing for feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.04.053 – volume: 200 start-page: 141 year: 2017 ident: 10.1016/j.asoc.2019.105946_b27 article-title: Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm publication-title: Appl. Energy doi: 10.1016/j.apenergy.2017.05.029 |
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| Title | Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis |
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