Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Theoretical Computer Science
Rough set Based Ensemble Classifier forWeb Page Classification
Fundamenta Informaticae
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An automatic method to determine the number of clusters using decision-theoretic rough set
International Journal of Approximate Reasoning
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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.