NDLI: Integration of classifier diversity measures for feature selection-based classifier ensemble reduction

Content Provider	Springer Nature Link
Author	Yao, Gang Zeng, Hualin Chao, Fei Su, Chang Lin, Chih Min Zhou, Changle
Copyright Year	2015
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 non-pairwise 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 non-pairwise 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.
Starting Page	2995
Ending Page	3005
Page Count	11
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	20
Issue Number	8
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-11-12
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classifier ensemble reduction Classifier diversity Classifier performance evaluation Harmony search algorithm Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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