Abstract
The One Class Classification (OCC) problem is different from the conventional binary/multi-class classification problem in the sense that in OCC, the negative class is either not present or not properly sampled. The problem of classifying positive (or target) cases in the absence of appropriately-characterized negative cases (or outliers) has gained increasing attention in recent years. Researchers have addressed the task of OCC by using different methodologies in a variety of application domains. In this paper we formulate a taxonomy with three main categories based on the way OCC has been envisaged, implemented and applied by various researchers in different application domains. We also present a survey of current state-of-the-art OCC algorithms, their importance, applications and limitations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Tax, D., Duin, R.: Uniform object generation for optimizing one-class classifiers. J. Machine Learning Research 2, 155–173 (2001)
Tax, D.: One Class Classification. PhD thesis, Delft University of Technology (2001)
Yu, H., Han, J., Chang, K.C.C.: Positive-example based learning for web page classification using svm. In: Proc. Eighth International Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 239–248 (2002)
Moya, M., Koch, M., Hostetler, L.: One-class classifier networks for target recognition applications. In: Proceedings World Congress on Neural Networks, pp. 797–801 (1993)
Ritter, G., Gallegos, M.: Outliers in statistical pattern recognition and an application to automatic chromosome classification. Pattern Recognition Letters 18, 525–539 (1997)
Bishop, C.: Novelty detection and neural network validation. IEEE Proceedings on Vision, Image and Signal Processing, Special Issue on Applications of Neural Networks 141(4), 217–222 (1994)
Japkowicz, N.: Concept-Learning in the absence of counterexamples: An autoassociation-based approach to classification. PhD thesis, New Brunswick Rutgers, The State University of New Jersey (1999)
Liu, B., Dai, Y., Li, X., Lee, W., Yu, P.: Building text classifiers using positive and unlabeled examples. In: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003) (2003)
Li, X., Liu, B.: Learning to classify texts using positive and unlabeled data. In: 18th International Joint Conf. on Artificial Intelligence (IJCAI 2003), pp. 587–594 (2003)
Lee, W., Liu, B.: Learning with positive and unlabeled examples using weighted logistic regression. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003) (2003)
Tax, D., Duin, R.: Data domain description using support vectors. In: Proc. ESAN 1999, Brussels, pp. 251–256 (1999)
Tax, D., Duin, R.: Support vector domain description. Pattern Recognition Letters 20, 1191–1199 (1999)
Scholkopf, B., Williamson, R., Smola, A., Taylor, J., Platt, J.: Support vector method for novelty detection. In: Solla, S.A., Leen, T., Muller, K. (eds.) Neural Information Processing Systems, pp. 582–588 (2000)
Blum Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of 11th Annual conference on Computation Learning Theory, pp. 92–100. ACM Press, New York (1998)
Valiant, L.: Theory of the learnable. ACM 27(11), 1134–1142 (1984)
Muggleton, S.: Learning from the positive data. Machine Learning (2001)
Skabar, A.: Single-class classifier learning using neural networks: An application to the prediction of mineral deposits. In: Proceedings of the Second International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2127–2132 (2003)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 1–47 (1998)
Scholkopf, B., Williamson, R., Smola, A., Taylor, J.: Sv estimation of a distributions support. In: Advances in Neural Information Processing Systems (1999)
Manevitz, L.M., Yousef, M.: One-class svms for document classification. Journal of Machine Learning Research 2, 139–154 (2001)
Benediktsson, J., Swain, P.: Consensus theoretic classification methods. IEEE Transactions on Systems, Man and Cybernetics 22(4), 688–704 (1992)
Yu, H.: Single-class classification with mapping convergence. Machine Learning 61(1), 49–69 (2005)
de Ridder, D., Tax, D., Duin, R.: An experimental comparison of one-class classification methods. In: Proceedings of the 4th Annual Conference of the Advacned School for Computing and Imaging, Delft (1998)
Manevitz, L., Yousef, M.: Document classification on neural networks using only positive examples. In: Proc. of 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 304–306 (2000)
De Comite, F., Denis, F., Gillerson, R., Letouzey, F.: Positive and unlabeled examples help learning. In: Watanabe, O., Yokomori, T. (eds.) ALT 1999. LNCS (LNAI), vol. 1720, pp. 219–230. Springer, Heidelberg (1999)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Letouzey, F., Denis, F., Gilleron, R.: Learning from positive and unlabeled examples. In: Algorithmic Learning Theory, 11th International Conference, Sydney, Australia (2000)
Wang, Q., Lopes, L.S., Tax, D.J.: Visual object recognition through one-class learning. In: International Conference on Image Analysis and Recognition, pp. 463–470 (2004)
Liu, B., Lee, W., Yu, P., Li, X.: Partially supervised classification of text documents. In: Proc. of ICML (2002)
Dempster, A.P., Laird, N.M., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society (1977)
Yu, H., Han, J., Chang, K.: PEBL: Web page classification without negative examples. IEEE Transactions on Knowledge and Data Engineering 16(1) (2004)
Rocchio, J.: Relevant feedback in information retrieval. In: Salton, G. (ed.) The SMART retrieval system- experiments in automatic document processing, NJ, Englewood Cliffs (1971)
Lang, K.: Newsweeder: Learning to filter netnews. In: ICML 1995 (1995)
Peng, T., Zuo, W., He, F.: Text classification from positive and unlabeled documents based on ga. In: Proc. of VECPAR 2006, Brazil (2006)
Koppel, M., Schler, J.: Authorship verification as a one-class classification problem. In: Proceedings of the twenty-first International Conference on Machine learning, Alberta, Canada, vol. 69 (2004)
Denis, F., Gilleron, R., Tommasi, M.: Text classification from positive and unlabeled examples. In: 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2002)
Zeng, Z., Fu, Y., Roisman, G.I., Wen, Z., Hu, Y., Huang, T.S.: One-class classification for spontaneous facial expression analysis. In: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, pp. 281–286 (2006)
Gardner, B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-class novelty detection for seizure analysis from intracranial eeg. Journal of Machine Learning Research 7, 1025–1044 (2006)
Spinosa, E.J., de Carvalho, A.C.P.L.F.: SVMs for novel class detection in bioinformatics. In: Brazilian Workshop on Bioinformatics, pp. 81–88 (2004)
Alashwal, H.T., Deris, S., Othman, R.M.: One-class support vector machines for protein-protein interactions prediction. International Journal Biomedical Sciences 1(2), 120–127 (2006)
Sun, D., Tran, Q., Duan, H., Zhang, G.: A novel method for chinese spam detection based on one-class support vector machine. Journal of Information and Computational Science 2(1), 109–114 (2005)
Li, K., Huang, H., Tian, S., Xu, W.: Improving one-class svm for anomaly detection. In: Proceedings of the second international conference on machine learning and cybernetics, pp. 2–5 (November 2003)
Perdisci, R., Gu, G., Lee, W.: Using an ensemble of one-class svm classifiers to harden payload-based anomaly detection systems. In: Sixth International Conference on Data Mining, pp. 488–498 (2006)
Shin, H.J., Eom, D.W., Kim, S.S.: One-class support vector machines: an application in machine fault detection and classification. Computers and Industrial Engineering 48(2), 395–408 (2005)
Wang, K., Stolfo, S.J.: One class training for masquerade detection. In: ICDM Workshop on Data Mining for Computer Security (2003)
Munroe, D.T., Madden, M.G.: Multi-class and single-class classification approaches to vehicle model recognition from images. In: Proc. AICS 2005: Irish Conference on Artificial Intelligence and Cognitive Science, Portstewart (2005)
Howley, T., Madden, M.G.: An evolutionary approach to automatic kernel construction. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 417–426. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khan, S.S., Madden, M.G. (2010). A Survey of Recent Trends in One Class Classification. In: Coyle, L., Freyne, J. (eds) Artificial Intelligence and Cognitive Science. AICS 2009. Lecture Notes in Computer Science(), vol 6206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17080-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-17080-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17079-9
Online ISBN: 978-3-642-17080-5
eBook Packages: Computer ScienceComputer Science (R0)