Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems.

Ruta, D. and Gabrys, B., 2001. Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems. In: Soft Computing and Intelligent Systems for Industry: Proceedings and Scientific Program : Fourth International ICSC Symposium 2001, 26-29 June, 2001, Paisley, Scotland, p. 50.

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Abstract

Combining classifiers by majority voting (MV) has recently emerged as an effective way of improving performance of individual classifiers. However, the usefulness of applying MV is not always observed and is subject to distribution of classification outputs in a multiple classifier system (MCS). Evaluation of MV errors (MVE) for all combinations of classifiers in MCS is a complex process of exponential complexity. Reduction of this complexity can be achieved provided the explicit relationship between MVE and any other less complex function operating on classifier outputs is found. Diversity measures operating on binary classification outputs (correct/incorrect) are studied in this paper as potential candidates for such functions. Their correlation with MVE, interpreted as the quality of a measure, is thoroughly investigated using artificial and real-world datasets. Moreover, we propose new diversity measure efficiently exploiting information coming from the whole MCS, rather than its part, for which it is applied.

Item Type:Conference or Workshop Item (Paper)
Subjects:Generalities > Computer Science and Informatics > Artificial Intelligence
Generalities > Computer Science and Informatics
Group:School of Design, Engineering & Computing
ID Code:8544
Deposited By:INVALID USER
Deposited On:20 Dec 2008 14:44
Last Modified:07 Mar 2013 15:02

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