Ensemble Pruning via Individual Contribution Ordering

DSpace/Manakin Repository

Search OPUS


Advanced Search

Browse

My Account

Show simple item record

dc.contributor.author Lu, Z
dc.contributor.author Wu, X
dc.contributor.author Zhu, X
dc.contributor.author Bongard, J
dc.contributor.editor Committee, PT
dc.date.accessioned 2012-02-02T11:12:00Z
dc.date.issued 2010-01
dc.identifier.citation Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 871 - 880
dc.identifier.isbn 978-1-4503-0055-1
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 classifierâs 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].
dc.publisher ACM
dc.relation.isbasedon 10.1145/1835804.1835914
dc.title Ensemble Pruning via Individual Contribution Ordering
dc.type Conference Proceeding
dc.parent Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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.conference ACM SIGKDD International Conference on Knowledge Discovery and Data
dc.for 170203 Knowledge Representation and Machine Learning
dc.personcode 100507
dc.personcode 107283
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.date.activity 2010-07-25
dc.location.activity Washington, DC, USA en_US
dc.description.keywords ensemble learning, ensemble pruning en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Uncategorised (ID: 363)
utslib.collection.history Closed (ID: 3)


Files in this item

This item appears in the following Collection(s)

Show simple item record