On a New Measure of Classifier Competence Applied to the Design of Multiclassifier Systems
This paper presents a new method for calculating competence of a classifier in the feature space. The idea is based on relating the response of the classifier with the response obtained by a random guessing. The measure of competence reflects this relation and rates the classifier with respect to the random guessing in a continuous manner. Two multiclassifier systems representing fusion and selection strategies were developed using proposed measure of competence. The performance of multiclassifiers was evaluated using five benchmark databases from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. Classification results obtained for three simple fusion methods and one multiclassifier system with selection strategy were used for a comparison. The experimental results showed that, regardless of the strategy used by the multiclassifier system, the classification accuracy has increased when the measure of competence was employed. The improvement was most significant for simple fusion methods (sum, product and majority vote). For all databases, two developed multiclassifier systems produced the best classification scores.
KeywordsFeature Space Local Accuracy Fusion Method Validation Dataset Correct Class
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