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Mathematical Problems in Engineering
Volume 2016, Article ID 3845131, 11 pages
Research Article

A Novel Margin-Based Measure for Directed Hill Climbing Ensemble Pruning

1School of Computer and Information Technology, Xinyang Normal University, Xinyang, Henan 464000, China
2School of Information Engineering, Zhengzhou University, Zhengzhou, Henan 450000, China

Received 10 February 2016; Revised 14 June 2016; Accepted 30 June 2016

Academic Editor: Simone Bianco

Copyright © 2016 Huaping Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Ensemble pruning is a technique to increase ensemble accuracy and reduce its size by choosing a subset of ensemble members to form a subensemble for prediction. Many ensemble pruning algorithms via directed hill climbing searching policy have been recently proposed. The key to the success of these algorithms is to construct an effective measure to supervise the search process. In this paper, we study the importance of individual classifiers with respect to an ensemble using margin theory proposed by Schapire et al. and obtain that ensemble pruning via directed hill climbing strategy should focus more on examples with small absolute margins as well as classifiers that correctly classify more examples. Based on this principle, we propose a novel measure called the margin-based measure to explicitly evaluate the importance of individual classifiers. Our experiments show that using the proposed measure to prune an ensemble leads to significantly better accuracy results compared to other state-of-the-art measures.