An analysis of diversity measures

Diversity among the base classifiers is deemed to be important when constructing a classifier ensemble. Numerous algorithms have been proposed to construct a good classifier ensemble by seeking both the accuracy of the base classifiers and the diversity among them. However, there is no generally acc...

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Vydáno v:	Machine learning Ročník 65; číslo 1; s. 247 - 271
Hlavní autoři:	Tang, E. K., Suganthan, P. N., Yao, X.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer 01.10.2006 Springer Nature B.V
Témata:	Applied sciences Artificial intelligence Computer science; control theory; systems Exact sciences and technology Studies Diversity measures, Margin distribution, Majority vote Interrater agreement Experimental design Classification Generalized diversity, Measure of difficulty, Entropy measure, Coincident failure diversity Classifier ensemble Disagreement measure, Double fault measure Learning algorithm Artificial intelligence KW variance Variance Aggregate model
ISSN:	0885-6125, 1573-0565
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Abstract	Diversity among the base classifiers is deemed to be important when constructing a classifier ensemble. Numerous algorithms have been proposed to construct a good classifier ensemble by seeking both the accuracy of the base classifiers and the diversity among them. However, there is no generally accepted definition of diversity, and measuring the diversity explicitly is very difficult. Although researchers have designed several experimental studies to compare different diversity measures, usually confusing results were observed. In this paper, we present a theoretical analysis on six existing diversity measures (namely disagreement measure, double fault measure, KW variance, inter-rater agreement, generalized diversity and measure of difficulty), show underlying relationships between them, and relate them to the concept of margin, which is more explicitly related to the success of ensemble learning algorithms. We illustrate why confusing experimental results were observed and show that the discussed diversity measures are naturally ineffective. Our analysis provides a deeper understanding of the concept of diversity, and hence can help design better ensemble learning algorithms.[PUBLICATION ABSTRACT]
AbstractList	Diversity among the base classifiers is deemed to be important when constructing a classifier ensemble. Numerous algorithms have been proposed to construct a good classifier ensemble by seeking both the accuracy of the base classifiers and the diversity among them. However, there is no generally accepted definition of diversity, and measuring the diversity explicitly is very difficult. Although researchers have designed several experimental studies to compare different diversity measures, usually confusing results were observed. In this paper, we present a theoretical analysis on six existing diversity measures (namely disagreement measure, double fault measure, KW variance, inter-rater agreement, generalized diversity and measure of difficulty), show underlying relationships between them, and relate them to the concept of margin, which is more explicitly related to the success of ensemble learning algorithms. We illustrate why confusing experimental results were observed and show that the discussed diversity measures are naturally ineffective. Our analysis provides a deeper understanding of the concept of diversity, and hence can help design better ensemble learning algorithms.[PUBLICATION ABSTRACT] Diversity among the base classifiers is deemed to be important when constructing a classifier ensemble. Numerous algorithms have been proposed to construct a good classifier ensemble by seeking both the accuracy of the base classifiers and the diversity among them. However, there is no generally accepted definition of diversity, and measuring the diversity explicitly is very difficult. Although researchers have designed several experimental studies to compare different diversity measures, usually confusing results were observed. In this paper, we present a theoretical analysis on six existing diversity measures (namely disagreement measure, double fault measure, KW variance, inter-rater agreement, generalized diversity and measure of difficulty), show underlying relationships between them, and relate them to the concept of margin, which is more explicitly related to the success of ensemble learning algorithms. We illustrate why confusing experimental results were observed and show that the discussed diversity measures are naturally ineffective. Our analysis provides a deeper understanding of the concept of diversity, and hence can help design better ensemble learning algorithms.
Author	Yao, X. Suganthan, P. N. Tang, E. K.
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Keywords	Diversity measures, Margin distribution, Majority vote Interrater agreement Experimental design Classification Generalized diversity, Measure of difficulty, Entropy measure, Coincident failure diversity Classifier ensemble Disagreement measure, Double fault measure Learning algorithm Artificial intelligence KW variance Variance Aggregate model
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Snippet	Diversity among the base classifiers is deemed to be important when constructing a classifier ensemble. Numerous algorithms have been proposed to construct a...
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SubjectTerms	Applied sciences Artificial intelligence Computer science; control theory; systems Exact sciences and technology Studies
Title	An analysis of diversity measures
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