Bagged ensembles with tunable parameters
Ensemble learning is a popular classification method where many individual simple learners contribute to a final prediction. Constructing an ensemble of learners has been shown to often improve prediction accuracy over a single learner. Bagging and boosting are the most common ensemble methods, each...
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| Published in | Computational intelligence Vol. 35; no. 1; pp. 184 - 203 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Blackwell Publishing Ltd
01.02.2019
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Ensemble learning is a popular classification method where many individual simple learners contribute to a final prediction. Constructing an ensemble of learners has been shown to often improve prediction accuracy over a single learner. Bagging and boosting are the most common ensemble methods, each with distinct advantages. While boosting methods are typically very tunable with numerous parameters, to date, the type of flexibility this allows has been missing for general bagging ensembles. In this paper, we propose a new tunable weighted bagged ensemble methodology, resulting in a very flexible method for classification. We explore the impact tunable weighting has on the votes of each learner in an ensemble and compare the results with pure bagging and the best known bagged ensemble method, namely, the random forest. |
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| AbstractList | Ensemble learning is a popular classification method where many individual simple learners contribute to a final prediction. Constructing an ensemble of learners has been shown to often improve prediction accuracy over a single learner. Bagging and boosting are the most common ensemble methods, each with distinct advantages. While boosting methods are typically very tunable with numerous parameters, to date, the type of flexibility this allows has been missing for general bagging ensembles. In this paper, we propose a new tunable weighted bagged ensemble methodology, resulting in a very flexible method for classification. We explore the impact tunable weighting has on the votes of each learner in an ensemble and compare the results with pure bagging and the best known bagged ensemble method, namely, the random forest. Abstract Ensemble learning is a popular classification method where many individual simple learners contribute to a final prediction. Constructing an ensemble of learners has been shown to often improve prediction accuracy over a single learner. Bagging and boosting are the most common ensemble methods, each with distinct advantages. While boosting methods are typically very tunable with numerous parameters, to date, the type of flexibility this allows has been missing for general bagging ensembles. In this paper, we propose a new tunable weighted bagged ensemble methodology, resulting in a very flexible method for classification. We explore the impact tunable weighting has on the votes of each learner in an ensemble and compare the results with pure bagging and the best known bagged ensemble method, namely, the random forest. |
| Author | Pham, Hieu Olafsson, Sigurdur |
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| Cites_doi | 10.1023/A:1010933404324 10.1016/j.neunet.2014.09.003 10.1007/978-3-642-15883-4_11 10.1002/sam.11196 10.1007/3-540-45014-9_1 10.1016/j.patcog.2009.05.010 10.1007/s10844-017-0446-7 10.1016/S0031-3203(02)00121-8 10.1109/TIT.1967.1053964 10.1002/sam.10061 10.1111/j.2517-6161.1958.tb00292.x 10.1007/978-3-540-74690-4_33 10.1016/j.csda.2007.06.012 10.1007/978-3-642-31537-4_13 10.1214/ss/1009212519 10.1109/ICMCS.2014.6911187 10.1007/BF00058655 10.1057/s41273-017-0063-z 10.1007/978-3-319-19369-4_4 10.1016/j.neucom.2017.08.035 10.1007/978-0-387-21579-2_9 10.1037/h0042519 10.1016/S0004-3702(02)00190-X 10.1007/11494683_32 10.1007/BF00994018 10.5267/j.dsl.2015.7.001 10.32614/CRAN.package.titanic 10.1609/aaai.v29i1.9634 10.1109/TSMCC.2011.2161285 10.1016/j.ijforecast.2014.12.001 10.1111/coin.12070 10.1109/ICEBE.2010.99 |
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| SubjectTerms | Bagging bias‐variance tradeoff Classification ensemble learning Parameters |
| Title | Bagged ensembles with tunable parameters |
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