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A novel diversity measure and classifier selection approach for generating ensemble classifiers

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dc.contributor.authorJan, M.Z.
dc.contributor.authorVerma, B.
dc.date.issued2019
dc.description.abstractAccuracy and diversity are considered to be the two deriving factors when it comes to generating an ensemble classifier. Focusing only on accuracy causes the ensemble classifier to suffer from <italic>diminishing returns</italic> and the ensemble accuracy tends to plateau; whereas focusing only on diversity causes the ensemble classifier to suffer in accuracy. Therefore, a balance must be maintained between the two for the ensemble classifier to achieve high classification accuracy. In this paper, we propose a novel diversity measure known as Misclassification Diversity (MD) and an Incremental Layered Classifier Selection (ILCS) approach to generate an ensemble classifier. The proposed approach ILCS-MD generates an ensemble classifier by incrementally selecting classifiers from the base classifier pool based on increasing accuracy and diversity. The benefits are in two folds 1) the generated ensemble classifier contains only those classifiers from the pool which can either maximize accuracy whilst maintaining or increasing the diversity, and 2) the generated ensemble classifier selects only a few classifiers from the base classifier pool thus reducing ensemble component size as well. The proposed approach is evaluated on 55 benchmark datasets taken from UCI and KEEL dataset repositories. The results are compared with five existing pairwise diversity measures, and existing state of the art ensemble classifier approaches. A significance test is also conducted to verify the significance of the results.
dc.identifier.citationIEEE Access, 2019; 7(8880589):156360-156373
dc.identifier.doi10.1109/ACCESS.2019.2949059
dc.identifier.issn2169-3536
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11541.2/145808
dc.language.isoen
dc.publisherIEEE
dc.relation.fundingARC DP160102369
dc.rightsCopyright 2013 IEEE. Open access. This work is licensed under a Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/)
dc.source.urihttps://doi.org/10.1109/ACCESS.2019.2949059
dc.subjectensemble classifiers
dc.subjectneural networks
dc.subjectmultiple classifiers learning
dc.subjectdiversity measures
dc.titleA novel diversity measure and classifier selection approach for generating ensemble classifiers
dc.typeJournal article
pubs.publication-statusPublished
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ror.mmsid9916480205801831

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