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

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 subs...

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Bibliographic Details
Published inSoft computing (Berlin, Germany) Vol. 20; no. 8; pp. 2995 - 3005
Main Authors Yao, Gang, Zeng, Hualin, Chao, Fei, Su, Chang, Lin, Chih-Min, Zhou, Changle
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2016
Springer Nature B.V
Subjects
Accuracy
Algorithms
Artificial Intelligence
Classification
Classifiers
Computational efficiency
Computational Intelligence
Computing time
Control
Datasets
Engineering
Feature selection
Focus
Mathematical Logic and Foundations
Mechatronics
Methods
Reduction
Robotics
Search algorithms
Classifier diversity
Classifier ensemble reduction
Classifier performance evaluation
Harmony search algorithm
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1927-7