Authors: Gang Yao Hualin Zeng Fei Chao Chang Su ChihMin Lin Changle Zhou
Publish Date: 2015/11/12
Volume: 20, Issue: 8, Pages: 2995-3005
Abstract
A classifier ensemble combines a set of individual classifier’s predictions to produce more accurate results than that of any single classifier system However one classifier ensemble with too many classifiers may consume a large amount of computational time This paper proposes a new ensemble subset evaluation method that integrates classifier diversity measures into a novel classifier ensemble reduction framework The framework converts the ensemble reduction into an optimization problem and uses the harmony search algorithm to find the optimized classifier ensemble Both pairwise and nonpairwise diversity measure algorithms are applied by the subset evaluation method For the pairwise diversity measure three conventional diversity algorithms and one new diversity measure method are used to calculate the diversity’s merits For the nonpairwise diversity measure three classical algorithms are used The proposed subset evaluation methods are demonstrated by the experimental data In comparison with other classifier ensemble methods the method implemented by the measurement of the interrater agreement exhibits a high accuracy prediction rate against the current ensembles’ performance In addition the framework with the new diversity measure achieves relatively good performance with less computational time
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