Abstract
Combining the results of a number of individually trained classification systems to obtain a more accurate classifier is a widely used technique in pattern recognition. In this article, we have introduced a rough set based meta classifier (RSM). Theoretical analysis of the proposed RSM is carried out in relation to Bayes classifier since Bayes classifier is the best classifier. It has been shown that the performance of the meta classifier is at least as good as the best constituent classifier, and if one of the base classifiers of RSM converges to Bayes then RSM converges to Bayes classifier. Experimental studies show that the meta classifier improves accuracy of classification and beats other ensemble approaches in accuracy by a decisive margin, thus demonstrating the theoretical results.
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Saha, S., Murthy, C.A., Pal, S.K. (2009). Rough Ensemble Classifier: A Comparative Study. In: Di Gesù, V., Pal, S.K., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2009. Lecture Notes in Computer Science(), vol 5571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02282-1_15
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DOI: https://doi.org/10.1007/978-3-642-02282-1_15
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