Abstract
The goal of one-class classification is to distinguish the target class from all the other classes using only training data from the target class. Because it is difficult for a single one-class classifier to capture all the characteristics of the target class, combining several one-class classifiers may be required. Previous research has shown that the Random Subspace Method (RSM), in which classifiers are trained on different subsets of the feature space, can be effective for one-class classifiers. In this paper we show that the performance by the RSM can be noisy, and that pruning inaccurate classifiers from the ensemble can be more effective than using all available classifiers. We propose to apply pruning to RSM of one-class classifiers using a supervised area under the ROC curve (AUC) criterion or an unsupervised consistency criterion. It appears that when the AUC criterion is used, the performance may be increased dramatically, while for the consistency criterion results do not improve, but only become more predictable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Biggio, B., Fumera, G., Roli, F.: Multiple classifier systems under attack. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 74–83. Springer, Heidelberg (2010)
Bradley, A.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Bryll, R., Gutierrez-Osuna, R., Quek, F.: Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognition 36(6), 1291–1302 (2003)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys 41(3), 1–58 (2009)
Cheplygina, V.: Random subspace method for one-class classifiers. Master’s thesis, Delft University of Technology (2010)
DD_tools, the Data Description toolbox for Matlab, http://prlab.tudelft.nl/david-tax/dd_tools.html
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1–30 (2006)
Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32(200), 675–701 (1937)
Ho, T.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (2002)
Lazarevic, A., Kumar, V.: Feature bagging for outlier detection. In: 11th ACM SIGKDD Int. Conf. on Knowledge Discovery in Data Mining, pp. 157–166 (2005)
Nanni, L.: Experimental comparison of one-class classifiers for online signature verification. Neurocomputing 69(7-9), 869–873 (2006)
Nemenyi, P.: Distribution-free multiple comparisons. Ph.D. thesis, Princeton (1963)
OC classifier results, http://homepage.tudelft.nl/n9d04/occ/index.html
Oza, N., Tumer, K.: Classifier ensembles: Select real-world applications. Information Fusion 9(1), 4–20 (2008)
PRtools, Matlab toolbox for Pattern Recognition, http://www.prtools.org
Rokach, L.: Collective-agreement-based pruning of ensembles. Computational Statistics & Data Analysis 53(4), 1015–1026 (2009)
Schapire, R., Freund, Y.: Experiments with a new boosting algorithm. In: 13th Int. Conf. on Machine Learning, p. 148. Morgan Kaufmann, San Francisco (1996)
Tax, D., Duin, R.: Combining one-class classifiers. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 299–308. Springer, Heidelberg (2001)
Tax, D.: One-class classification; Concept-learning in the absence of counter-examples. Ph.D. thesis, Delft University of Technology (June 2001)
Tax, D., Muller, K.: A consistency-based model selection for one-class classification. In: 17th Int. Conf. on Pattern Recognition, pp. 363–366. IEEE, Los Alamitos (2004)
UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
Zhou, Z., Wu, J., Tang, W.: Ensembling neural networks: Many could be better than all. Artificial Intelligence 137(1-2), 239–263 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheplygina, V., Tax, D.M.J. (2011). Pruned Random Subspace Method for One-Class Classifiers. In: Sansone, C., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2011. Lecture Notes in Computer Science, vol 6713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21557-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-21557-5_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21556-8
Online ISBN: 978-3-642-21557-5
eBook Packages: Computer ScienceComputer Science (R0)