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Design of One-Class Classifier Using Hyper-Rectangles
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 Title & Authors
Design of One-Class Classifier Using Hyper-Rectangles
Jeong, In Kyo; Choi, Jin Young;
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Recently, the importance of one-class classification problem is more increasing. However, most of existing algorithms have the limitation on providing the information that effects on the prediction of the target value. Motivated by this remark, in this paper, we suggest an efficient one-class classifier using hyper-rectangles (H-RTGLs) that can be produced from intervals including observations. Specifically, we generate intervals for each feature and integrate them. For generating intervals, we consider two approaches : (i) interval merging and (ii) clustering. We evaluate the performance of the suggested methods by computing classification accuracy using area under the roc curve and compare them with other one-class classification algorithms using four datasets from UCI repository. Since H-RTGLs constructed for a given data set enable classification factors to be visible, we can discern which features effect on the classification result and extract patterns that a data set originally has.
Hyper-Rectangles;One-Class Classification;Interval Merging;Interval Conjunction;Clustering;
 Cited by
Asuncion, A. and Newman, D. (2007), UCI machine learning repository, URL

Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and Muller, K. R. (2010), How to explain individual classification decisions, The Journal of Machine Learning Research, 11, 1803- 1831.

Bosco, G. L. and Pinello, L. (2009), A fuzzy one class classifier for multi layer model, Fuzzy Logic and Application, Lecture Notes in Computer Science, 5571, 124-131.

Breiman, L. (2001), Random forests, Machine Learning, 45(1), 5-32. crossref(new window)

Breiman, L., Friedman, J., Olshen, R., Stone, C., Steinberg, D., and Colla, P. (1983), CART : Classification and regression trees, Wadsworth : Belmont, CA, 156.

Cortes, C. and Vapnik, V. (1995), Support-vector networks, Machine Learning, 20(3), 273-297.

Desir, C., Bernard, S., Petitjean, C., and Heutte, L. (2012), A random forest based approach for one class classification in medical imaging, Machine Learning in Medical Imaging, Lecture Notes in Computer Science, 7588, 250-257.

Domingos, P. and Hulten, G. (2000), Mining high-speed data streams, Proceedings of the 2000 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71-80.

Hullermeier, E. (2011), Fuzzy sets in machine learning and data mining, Applied Soft Computing, 11(2), 1493-1505. crossref(new window)

Jeong, I. K. and Choi, J. Y. (2015), One-class classification using hyper-rectangles, Proceedings of the KORMS/KIIE/ESK/KSS 2015 Spring Conference, Jeju, 2265-2276.

Juszczak, P., Tax, D. M. J., Pekalska, E., and Duin, R. P. W. (2009), Minimum spanning tree based one-class classifier, Neurocomputing, 72(7-9), 1859-1869. crossref(new window)

Kemmler, M., Rodner, E., Wacker, E.-S., and Denzler, J. (2013), One-class classification with gaussian processes, Pattern Recognition, 46(12), 3507-3518. crossref(new window)

Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., and Meltzer, P. S. (2001), Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nature medicine, 7(6), 673-679. crossref(new window)

Khan, S. and Madden, M. G. (2014), One-class classification : taxonomy of study and review of techniques, The Knowledge Engineering Review, 29(3), 345-374. crossref(new window)

Letouzey, F., Denis, F., and Gilleron, R. (2000), Learning from positive and unlabeled examples, Proceedings of 11th International Conference on Algorithmic Learning Theory, Sydney, Australia.

Li, C., Zhang, Y., and Li, X. (2009), OcVFDT : one-class very fast decision tree for one-class classification of data streams, Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, 79-86.

MacQueen, J. (1967), Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1(14), 281-297.

Manevitz, L. and Yousef, M. (2000), Document classification on neural networks using only positive Examples, Proceedings of 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 304-306.

Manevitz, L. and Yousef, M. (2007), One-class document classification via Neural Networks, Neurocomputing, 70, 1466-1481. crossref(new window)

Scholkopf, B., Williamson, R., Smola, A., Taylor, J. S. and Platt, J. (2000), Support vector method for novelty detection, Advances in Neural Information Processing Systems, 12, 582-588.

Schmidhuber, J. (2015), Deep learning in neural networks : An overview, Neural Networks, 61, 85-117. crossref(new window)

Skabar, A. (2003), Single-class classifier learning using neural networks : an application to the prediction of mineral deposits, Proceedings of the Second International Conference on Machine Learning and Cybernetics, 4, 2127-2132.

Tax, D. M. J. and Duin, R. P. W. (1999a), Data domain description using support vectors, Proceedings of European Sysmposium on Artificial Neural Networks, Brussels, 251-256.

Tax, D. M. J. and Duin, R. P. W. (1999b), Support vector domain description, Pattern Recognition Letters, 20, 1191-1199. crossref(new window)

Tax, D. M. J. (2001), One-class Classification, PhD thesis, Delft University of Technology.

Tax, D. M. J. (2010), One-class classifier results, URL

Quinlan, J. R. (1993), C4.5 : Programs for Machine Learning, Morgan Kaufmann, California.

Utkin, L. V. (2012), Fuzzy one-class classification model using contamination neighborhoods, Advances in Fuzzy Systems, 22, doi: 10.1155/2012/984325.