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http://dx.doi.org/10.7232/JKIIE.2015.41.5.439

Design of One-Class Classifier Using Hyper-Rectangles  

Jeong, In Kyo (Department of Industrial Engineering, Ajou University)
Choi, Jin Young (Department of Industrial Engineering, Ajou University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.41, no.5, 2015 , pp. 439-446 More about this Journal
Abstract
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.
Keywords
Hyper-Rectangles; One-Class Classification; Interval Merging; Interval Conjunction; Clustering;
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1 Asuncion, A. and Newman, D. (2007), UCI machine learning repository, URL http://www.ics.uci.edu/-mlearn/MLRepository.html.
2 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.
3 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.
4 Breiman, L. (2001), Random forests, Machine Learning, 45(1), 5-32.   DOI
5 Breiman, L., Friedman, J., Olshen, R., Stone, C., Steinberg, D., and Colla, P. (1983), CART : Classification and regression trees, Wadsworth : Belmont, CA, 156.
6 Cortes, C. and Vapnik, V. (1995), Support-vector networks, Machine Learning, 20(3), 273-297.   DOI
7 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.
8 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.
9 Hullermeier, E. (2011), Fuzzy sets in machine learning and data mining, Applied Soft Computing, 11(2), 1493-1505.   DOI
10 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.
11 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.   DOI
12 Kemmler, M., Rodner, E., Wacker, E.-S., and Denzler, J. (2013), One-class classification with gaussian processes, Pattern Recognition, 46(12), 3507-3518.   DOI
13 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.   DOI
14 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.   DOI
15 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.
16 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.
17 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.
18 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.
19 Manevitz, L. and Yousef, M. (2007), One-class document classification via Neural Networks, Neurocomputing, 70, 1466-1481.   DOI
20 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.
21 Schmidhuber, J. (2015), Deep learning in neural networks : An overview, Neural Networks, 61, 85-117.   DOI
22 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.
23 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.
24 Tax, D. M. J. and Duin, R. P. W. (1999b), Support vector domain description, Pattern Recognition Letters, 20, 1191-1199.   DOI
25 Tax, D. M. J. (2001), One-class Classification, PhD thesis, Delft University of Technology.
26 Tax, D. M. J. (2010), One-class classifier results, URL http://homepage.tudelft.nl/n9d04/occ/.
27 Quinlan, J. R. (1993), C4.5 : Programs for Machine Learning, Morgan Kaufmann, California.
28 Utkin, L. V. (2012), Fuzzy one-class classification model using contamination neighborhoods, Advances in Fuzzy Systems, 22, doi: 10.1155/2012/984325.