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Dynamic classifier selection for one-class classification
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DOI：10.1016/j.knosys.2016.05.054.png)
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One-class classification is among the most difficult areas of the contemporary machine learning. The main problem 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 classifier quality. Therefore ensemble methods that can use more than one model, are a highly attractive solution. With an ensemble approach, we prevent the situation of choosing the weakest model and usually improve the robustness of our recognition system. However, one cannot assume that all classifiers available in the pool are in general accurate - they may have local competence areas in which they should be employed. In this work, we present a dynamic classifier selection method for constructing efficient one-class ensembles. We propose to calculate the competencies of all classifiers for a given validation example and use them to estimate their competencies over the entire decision space with the Gaussian potential function. We introduce three measures of classifier's competence designed specifically for one-class problems. Comprehensive experimental analysis, carried on a number of benchmark data and backed-up with a thorough statistical analysis prove the usefulness of the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
One-class classification
Classifier ensemble
Machine learning
Dynamic classifier selection
Competence measure
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7.6
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1.2W
被引数:
4.5W
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分享收藏分享收藏Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers使用基本分类器的能力和多样性度量对集成分类器进行最佳选择.png)
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