One-Class Classification Ensemble with Dynamic Classifier Selection
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The main problem of one-class classification lies in selecting the model for the data, as we do not have any access to counterexamples, and cannot use standard methods for estimating the quality of the classifier. Therefore ensemble methods that can utilize more than one model, are a highly attractive solution which prevents the situation of choosing the weakest model and improves the robustness of our recognition system. However, one cannot assume that all classifiers available in the pool are in general accurate - they may have some local areas of competence in which they should be utilized. We present a dynamic classifier selection method for constructing efficient one-class ensembles. We propose to calculate the individual classifier competence in a given validation point and use them to estimate competence of each classifier over the entire decision space with a Gaussian potential function. Experimental analysis, carried on a number of benchmark data and backed-up with a thorough statistical analysis prove its usefulness.
KeywordsOne-class classification Classifier selection Competence measure
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