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.
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References
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36, 105–139 (1999)
Bicego, M., Figueiredo, M.A.T.: Soft clustering using weighted one-class support vector machines. Pattern Recognition 42, 27–32 (2009)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Di Marzio, M., Taylor, C.C.: Boosting kernel density estimates: a bias reduction technique? Biometrika 91, 226–233 (2004)
Hempstalk, K., Frank, E., Witten, I.H.: One-class classification by combining density and class probability estimation. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 505–519. Springer, Heidelberg (2008)
Hoffmann, H.: Kernel PCA for novelty detection. Pattern Recognition 40, 863–874 (2007)
Parzen, E.: On estimation of a probability density function and mode. Annals of Mathematical Statistics 33, 1065–1076 (1962)
Perdisci, R., Gu, G., Lee, W.: Using an ensemble of one-class SVM classifiers to harden payload-based anomaly detection systems. In: 6th IEEE International Conference on Data Mining, pp. 488–498. IEEE Press, New York (2006)
Li, C., Zhang, Y.: Bagging one-class decision trees. In: 5th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 420–423. IEEE Press, New York (2008)
Roth, V.: Kernel fisher discriminants for outlier detection. Neural Computation 18, 942–960 (2006)
Scholköpf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation 13, 1443–1471 (2001)
Tax, D.M.J., Duin, R.P.W.: Support vector domain description. Pattern Recognition Letters 20, 1191–1999 (1999)
Tax, D.M.J.: One-class classification. Ph.D thesis, Delft University of Technology (2001)
Tax, D.M.J., Duin, R.P.W.: Combining one-class classifiers. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 299–308. Springer, Heidelberg (2001)
Tax, D.M.J., Juszczak, P.: Kernel whitening for one-class classification. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 40–52. Springer, Heidelberg (2002)
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Shieh, A.D., Kamm, D.F. (2009). Ensembles of One Class Support Vector Machines. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_19
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DOI: https://doi.org/10.1007/978-3-642-02326-2_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02325-5
Online ISBN: 978-3-642-02326-2
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