publication . Preprint . 2019

Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability

Das, Shubhomoy; Islam, Md Rakibul; Jayakodi, Nitthilan Kannappan; Doppa, Janardhan Rao;
Open Access English
  • Published: 23 Jan 2019
Abstract
Comment: 47 pages including appendix; code is available at https://github.com/shubhomoydas/ad_examples. arXiv admin note: substantial text overlap with arXiv:1809.06477
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
Download from

Emmott, A., Das, S., Dietterich, T. G., Fern, A., & Wong, W. (2015). Systematic construction of anomaly detection benchmarks from real data. CoRR, abs/1503.01158.

Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Machine learning, 28 (2-3), 133{168.

Furnkranz, J., Gamberger, D., & Lavrac, N. (2012). Foundations of Rule Learning. Cognitive Technologies. Springer.

Gornitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Arti cial Intelligence Research (JAIR), 46, 235{262.

Guha, S., Mishra, N., Roy, G., & Schrijvers, O. (2016). Robust random cut forest based anomaly detection on streams. In Proceedings of the 33nd International Conference on Machine Learning, (ICML), pp. 2712{2721.

Kalai, A. T., Klivans, A. R., Mansour, Y., & Servedio, R. A. (2008). Agnostically learning halfspaces. SIAM Journal on Computing, 37 (6), 1777{1805. [OpenAIRE]

Yan, S., & Zhang, C. (2017). Revisiting perceptron: E cient and label-optimal learning of halfspaces. In Advances in Neural Information Processing Systems (NeurIPS), pp. 1056{1066.

Abstract
Comment: 47 pages including appendix; code is available at https://github.com/shubhomoydas/ad_examples. arXiv admin note: substantial text overlap with arXiv:1809.06477
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
Download from

Emmott, A., Das, S., Dietterich, T. G., Fern, A., & Wong, W. (2015). Systematic construction of anomaly detection benchmarks from real data. CoRR, abs/1503.01158.

Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Machine learning, 28 (2-3), 133{168.

Furnkranz, J., Gamberger, D., & Lavrac, N. (2012). Foundations of Rule Learning. Cognitive Technologies. Springer.

Gornitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Arti cial Intelligence Research (JAIR), 46, 235{262.

Guha, S., Mishra, N., Roy, G., & Schrijvers, O. (2016). Robust random cut forest based anomaly detection on streams. In Proceedings of the 33nd International Conference on Machine Learning, (ICML), pp. 2712{2721.

Kalai, A. T., Klivans, A. R., Mansour, Y., & Servedio, R. A. (2008). Agnostically learning halfspaces. SIAM Journal on Computing, 37 (6), 1777{1805. [OpenAIRE]

Yan, S., & Zhang, C. (2017). Revisiting perceptron: E cient and label-optimal learning of halfspaces. In Advances in Neural Information Processing Systems (NeurIPS), pp. 1056{1066.

Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue