Ensemble Pruning via Individual Contribution Ordering

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dc.contributor.author Lu, Zhenyu en_US
dc.contributor.author Wu, Xindong en_US
dc.contributor.author Zhu, Xingquan en_US
dc.contributor.author Bongard, Josh en_US
dc.contributor.editor Programme Technical Committee en_US
dc.date.accessioned 2012-02-02T11:12:00Z
dc.date.available 2012-02-02T11:12:00Z
dc.date.issued 2010 en_US
dc.identifier 2010001754 en_US
dc.identifier.citation Lu Zhenyu et al. 2010, 'Ensemble Pruning via Individual Contribution Ordering', , ACM, USA, , pp. 871-880. en_US
dc.identifier.issn 978-1-4503-0055-1 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16715
dc.description.abstract An ensemble is a set of learned models that make decisions collectively. Although an ensemble is usually more accurate than a single learner, existing ensemble methods often tend to construct unnecessarily large ensembles, which increases the memory consumption and computational cost. Ensemble pruning tackles this problem by selecting a subset of ensemble members to form subensembles that are subject to less resource consumption and response time with accuracy that is similar to or better than the original ensemble. In this paper, we analyze the accuracy/diversity trade-off and prove that classifiers that are more accurate and make more predictions in the minority group are more important for subensemble construction. Based on the gained insights, a heuristic metric that considers both accuracy and diversity is proposed to explicitly evaluate each individual classifiera??s contribution to the whole ensemble. By incorporating ensemble members in decreasing order of their contributions, subensembles are formed such that users can select the top p percent of ensemble members, depending on their resource availability and tolerable waiting time, for predictions. Experimental results on 26 UCI data sets show that subensembles formed by the proposed EPIC (Ensemble Pruning via Individual Contribution ordering) algorithm outperform the original ensemble and a state-ofthe-art ensemble pruning method, Orientation Ordering (OO) [16]. en_US
dc.language English en_US
dc.publisher ACM en_US
dc.relation.isbasedon http://dx.doi.org/10.1145/1835804.1835914 en_US
dc.title Ensemble Pruning via Individual Contribution Ordering en_US
dc.parent Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 871 en_US
dc.identifier.endpage 880 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.for 170203 en_US
dc.personcode 0000066561 en_US
dc.personcode 100507 en_US
dc.personcode 107283 en_US
dc.personcode 0000066562 en_US
dc.percentage 100 en_US
dc.classification.name Knowledge Representation and Machine Learning en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom ACM SIGKDD International Conference on Knowledge Discovery and Data en_US
dc.date.activity 20100725 en_US
dc.location.activity Washington, DC, USA en_US
dc.description.keywords ensemble learning, ensemble pruning en_US

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