Ensembles of One Class Support Vector Machines

Authors:
Albert D. Shieh;David F. Kamm
Affiliations:
Department of Statistics, Harvard University, Cambridge, USA MA 02138;Department of Computer Science, Stanford University, Stanford, USA CA 94305
Venue:
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Year:
2009

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Abstract

The one class support vector machine (OCSVM) is a widely used approach to one class classification, the problem of distinguising one class of data from the rest of the feature space. However, even with optimal parameter selection, the OCSVM can be sensitive to overfitting in the presence of noise. Bagging is an ensemble method that can reduce the influence of noise and prevent overfitting. In this paper, we propose a bagging OCSVM using kernel density estimation to decrease the weight given to noise. We demonstrate the improved performance of the bagging OCSVM on both simulated and real world data sets.