A Kind of Combination Feature Division and Diversity Measure of Multi-Classifier Selective Ensemble Algorithm

Abstract:

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In ensemble learning, in order to improve the performance of individual classifiers and the diversity of classifiers, from the classifiers generation and combination, this paper proposes a kind of combination feature division and diversity measure of multi-classifier selective ensemble algorithm. The algorithm firstly applied bagging method to create some feature subsets, Secondly using principal component analysis of feature extraction method on each feature subsets, then select classifiers with high-classification accuracy; finally before classifier combination we use classifier diversity measure method select diversity classifiers. Experimental results prove that classification accuracy of the algorithm is obviously higher than popular bagging algorithm.

Info:

Periodical:

Edited by:

Helen Zhang and David Jin

Pages:

55-58

DOI:

10.4028/www.scientific.net/AMM.63-64.55

Citation:

Y. Wang et al., "A Kind of Combination Feature Division and Diversity Measure of Multi-Classifier Selective Ensemble Algorithm", Applied Mechanics and Materials, Vols. 63-64, pp. 55-58, 2011

Online since:

June 2011

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Price:

$35.00

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