Ensemble pruning via individual contribution ordering

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 contribution...

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Zhenyu Lu, Xindong Wu, Xingquan Zhu, Josh Bongard
• Keywords: Data Mining, Ensemble Members, Ensemble Methods, KDD 2010, Original Ensemble