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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Veröffentlicht in:	Machine learning Jg. 65; H. 1; S. 247 - 271
Hauptverfasser:	Tang, E. K., Suganthan, P. N., Yao, X.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer 01.10.2006 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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