1 |
Asuncion, A. and Newman, D., UCI machine learning repository, http://www.ics.uci.edu/-mlearn/MLRepository.html.
|
2 |
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and Muller, K.R., How to explain individual classification decisions, The Journal of Machine Learning Research, 2010, Vol. 11, pp. 1803-1831.
|
3 |
Barakat, N. and Bradley, A.P., Rule extraction from support vector machines : a review, Neurocomputing, 2010, Vol. 74, No. 1-3, pp. 178-190.
DOI
|
4 |
Cortes, C. and Vapnik, V., Support-vector networks, Machine Learning, 1995, Vol. 20, No. 3, pp. 273-297.
DOI
|
5 |
De Comite, F., Denis, F., Gilleron, R., and Letouzey, F., Positive and unlabeled examples help learning, Proceedings of International Conference on Algorithmic Learning Theory, 1999, Berlin, Germany, pp. 219-230.
|
6 |
De Ridder, D., Tax, D., and Duin, R.P., An experimental comparison of one-class classification methods, the 4th Annual Conference of the Advanced School for Computing and Imaging, 1998, Delft, Netherlands.
|
7 |
Juszczak, P., Tax, D.M., Pe, E., and Duin, R.P., Minimum spanning tree based one-class classifier, Neurocomputing, 2009, Vol. 72, No. 7-9, pp. 1859-1869.
DOI
|
8 |
Desir, C., Bernard, S., Petitjean, C., and Heutte, L., A random forest based approach for one class classification in medical imaging, Machine Learning in Medical Imaging, Lecture Notes in Computer Science, 2012, Vol. 7588, pp. 250-257.
|
9 |
Hempstalk, K., Frank, E., and Witten, I.H., One-class classification by combining density and class probability estimation, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2008, Berlin, Germany, pp. 505-519.
|
10 |
Jeong, I.K. and Choi, J.Y., Design of One-Class Classifier Using Hyper-Rectangles, Journal of the Korean Institute of Industrial Engineers, 2015, Vol. 41, No. 5, pp. 439-446.
DOI
|
11 |
Kang, B.S. and Kim, S.S., Combined Artificial Bee Colony for Data Clustering, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 40, No. 4, pp. 203-210.
DOI
|
12 |
Letouzey, F., Denis, F., and Gilleron, R., Learning from positive and unlabeled examples, Proceedings of 10th International Conference on Algorithmic Learning Theory, Berlin, German, 2000, pp. 71-85.
|
13 |
Park, Y.J., Kim, G.Y., and Jang, S.W., Traffic Anomaly Identification Using Multi-Class Support Vector Machine, Journal of the Korea Academia-Industrial Cooperation Society, 2013, Vol. 14, No. 4, pp. 1942-1950.
DOI
|
14 |
Scholkopf, B., Williamson, R., Smola, A., Taylor, J.S., and Platt, J., Support vector method for novelty detection, Advances in Neural Information Processing Systems, 2000, Vol. 12, pp. 582-588.
|
15 |
Tax, D.M.J., One-class Classification, [dissertation], [Delft, Netherlands] : Delft University of Technology, 2001.
|
16 |
Tarassenko, L., Hayton, P., Cerneaz, N., and Brady, M., Novelty detection for the identification of masses in mammograms, 4th International Conference on Artificial Neural Networks, 1995, pp. 442-447.
|
17 |
Tax, D.M.J. and Duin, R.P.W., Data domain description using support vectors, Proceedings of European Symposium on Artificial Neural Networks, 1999a, Brussels, Belgium, pp. 251-256.
|
18 |
Tax, D.M.J. and Duin, R.P.W., Support vector domain description, Pattern Recognition Letters, 1999b, Vol. 20, pp. 1191-1199.
DOI
|