Selective ensemble of support vector data descriptions for novelty detection

Authors:
Hong-Jie Xing;Xue-Fang Chen
Affiliations:
Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science, Hebei University, Baoding, Hebei Province, China;Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science, Hebei University, Baoding, Hebei Province, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Year:
2012

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Abstract

Since support vector data description (SVDD) is regarded as a strong classifier, the traditional ensemble methods are not fit for directly combining the results of several SVDDs. Moreover, as is well-known, when many trained classifiers are available, it is better to ensemble some of them rather than all. In this paper, a selective ensemble method based on correntropy is proposed to deal with the foresaid problems. The base classifier used in the proposed ensemble is SVDD. Experimental results on two synthetic data sets and five benchmark data sets demonstrate that the proposed method is superior to its related approaches.