Commenced in January 2007
Paper Count: 30127
Semi-Supervised Outlier Detection Using a Generative and Adversary Framework
Abstract:In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474926Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 478
 V. Hodge and J. Austin, “A survey of outlier detection methodologies,” Artificial intelligence review, vol. 22, no. 2, pp. 85–126, 2004.
 M. M. Moya, M. W. Koch, and L. D. Hostetler, “One-class classifier networks for target recognition applications,” Sandia National Labs., Albuquerque, NM (United States), Tech. Rep., 1993.
 G. Ritter and M. T. Gallegos, “Outliers in statistical pattern recognition and an application to automatic chromosome classification,” Pattern Recognition Letters, vol. 18, no. 6, pp. 525–539, 1997.
 C. M. Bishop, “Novelty detection and neural network validation,” IEE Proceedings-Vision, Image and Signal processing, vol. 141, no. 4, pp. 217–222, 1994.
 N. Japkowicz, “Concept-learning in the absence of counter-examples: an autoassociation-based approach to classification,” Ph.D. dissertation, Rutgers, The State University of New Jersey, 1999.
 S. Basu, M. Bilenko, and R. J. Mooney, “A probabilistic framework for semi-supervised clustering,” in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004, pp. 59–68.
 B. Agarwal et al., “One-class support vector machine for sentiment analysis of movie review documents,” World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 9, no. 12, pp. 2458–2461, 2015.
 A. Lakhina, M. Crovella, and C. Diot, “Mining anomalies using traffic feature distributions,” in ACM SIGCOMM Computer Communication Review, vol. 35. ACM, 2005, pp. 217–228.
 Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
 S. Zhai, Y. Cheng, W. Lu, and Z. Zhang, “Deep structured energy based models for anomaly detection,” arXiv preprint arXiv:1605.07717, 2016.
 J. Xie, R. Girshick, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in International conference on machine learning, 2016, pp. 478–487.
 X. Yang, K. Huang, J. Y. Goulermas, and R. Zhang, “Joint learning of unsupervised dimensionality reduction and gaussian mixture model,” Neural Processing Letters, vol. 45, no. 3, pp. 791–806, 2017.
 I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems, 2014, pp. 2672–2680.
 T. Schlegl, P. Seeb¨ock, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs, “Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,” in International Conference on Information Processing in Medical Imaging. Springer, 2017, pp. 146–157.
 Y. Yu, W.-Y. Qu, N. Li, and Z. Guo, “Open-category classification by adversarial sample generation,” arXiv preprint arXiv:1705.08722, 2017.
 H. Zenati, C. S. Foo, B. Lecouat, G. Manek, and V. R. Chandrasekhar, “Efficient gan-based anomaly detection,” arXiv preprint arXiv:1802.06222, 2018.
 M. A. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko, “A review of novelty detection,” Signal Processing, vol. 99, pp. 215–249, 2014.
 B. Lindsay, G. L. Mclachlan, K. E. Basford, and M. Dekker, “Mixture models: Inference and applications to clustering,” Journal of the American Statistical Association, vol. 84, no. 405, p. 337, 1989.
 C. M. Bishop, Pattern recognition and machine learning. springer, 2006.
 E. Parzen, “On estimation of a probability density function and mode,” The annals of mathematical statistics, vol. 33, no. 3, pp. 1065–1076, 1962.
 P. Vincent and Y. Bengio, “Manifold parzen windows,” in Advances in neural information processing systems, 2003, pp. 849–856.
 Y. Bengio, H. Larochelle, and P. Vincent, “Non-local manifold parzen windows,” in Advances in neural information processing systems, 2006, pp. 115–122.
 M. Markou and S. Singh, “Novelty detection: a review—part 2:: neural network based approaches,” Signal processing, vol. 83, no. 12, pp. 2499–2521, 2003.
 J. An and S. Cho, “Variational autoencoder based anomaly detection using reconstruction probability,” Special Lecture on IE, vol. 2, pp. 1–18, 2015.
 D. M. Tax and K.-R. M¨uller, “Feature extraction for one-class classification,” Lecture notes in computer science, pp. 342–349, 2003.
 S. D. Bay and M. Schwabacher, “Mining distance-based outliers in near linear time with randomization and a simple pruning rule,” in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2003, pp. 29–38.
 M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “Lof: identifying density-based local outliers,” in ACM sigmod record, vol. 29. ACM, 2000, pp. 93–104.
 D. Barbar´a, Y. Li, and J. Couto, “Coolcat: an entropy-based algorithm for categorical clustering,” in Proceedings of the eleventh international conference on Information and knowledge management. ACM, 2002, pp. 582–589.
 Z. He, X. Xu, and S. Deng, “Discovering cluster-based local outliers,” Pattern Recognition Letters, vol. 24, no. 9, pp. 1641–1650, 2003.
 B. Sch¨olkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt, “Support vector method for novelty detection,” in Advances in neural information processing systems, 2000, pp. 582–588.
 D. M. Tax and R. P. Duin, “Support vector domain description,” Pattern recognition letters, vol. 20, no. 11, pp. 1191–1199, 1999.
 K. Hempstalk, E. Frank, and I. H. Witten, “One-class classification by combining density and class probability estimation,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2008, pp. 505–519.
 W. Fan, M. Miller, S. Stolfo, W. Lee, and P. Chan, “Using artificial anomalies to detect unknown and known network intrusions,” Knowledge and Information Systems, vol. 6, no. 5, pp. 507–527, 2004.
 N. Abe, B. Zadrozny, and J. Langford, “Outlier detection by active learning,” in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006, pp. 504–509.
 D. M. Tax and R. P. Duin, “Uniform object generation for optimizing one-class classifiers,” Journal of machine learning research, vol. 2, no. Dec, pp. 155–173, 2001.
 A. B´anhalmi, A. Kocsor, and R. Busa-Fekete, “Counter-example generation-based one-class classification,” in ECML. Springer, 2007, pp. 543–550.
 J. Zhao, M. Mathieu, and Y. LeCun, “Energy-based generative adversarial network,” arXiv preprint arXiv:1609.03126, 2016.
 S. Mohamed and B. Lakshminarayanan, “Learning in implicit generative models,” arXiv preprint arXiv:1610.03483, 2016.
 S. Nowozin, B. Cseke, and R. Tomioka, “f-gan: Training generative neural samplers using variational divergence minimization,” in Advances in Neural Information Processing Systems, 2016, pp. 271–279.
 M. Uehara, I. Sato, M. Suzuki, K. Nakayama, and Y. Matsuo, “Generative adversarial nets from a density ratio estimation perspective,” arXiv preprint arXiv:1610.02920, 2016.
 M. Lichman et al., “Uci machine learning repository,” 2013.
 B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen, “Deep autoencoding gaussian mixture model for unsupervised anomaly detection,” 2018.
 L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research, vol. 9, no. Nov, pp. 2579–2605, 2008.