NDLI: Ensemble pruning via individual contribution ordering

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Ensemble pruning via individual contribution ordering

Content Provider	ACM Digital Library
Author	Lu, Zhenyu Wu, Xindong Bongard, Josh Zhu, Xingquan
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 classifier'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-of-the-art ensemble pruning method, Orientation Ordering (OO).
Starting Page	871
Ending Page	880
Page Count	10
File Format	PDF QT / MOV
ISBN	9781450300551
DOI	10.1145/1835804.1835914
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ensemble pruning Ensemble learning
Content Type	Video Text
Resource Type	Article

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