a novel margin-based measure for directed hill climbing ensemble pruning

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ID: 184709
2016
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Abstract
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.
Reference Key
guo2016mathematicala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Huaping Guo;Fang Sun;Jiong Cheng;Yanling Li;Mingling Xu
Journal journal of power sources
Year 2016
DOI
10.1155/2016/3845131
URL
http://dx.doi.org/10.1155/2016/3845131
Keywords
civil engineering (general)mathematics engineering (general)

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