publication . Preprint . 2018

Deep Active Learning for Anomaly Detection

Pimentel, Tiago; Monteiro, Marianne; Veloso, Adriano; Ziviani, Nivio;
Open Access English
  • Published: 23 May 2018
Abstract
Anomalies are intuitively easy for human experts to understand, but they are hard to define mathematically. Therefore, in order to have performance guarantees in unsupervised anomaly detection, priors need to be assumed on what the anomalies are. By contrast, active learning provides the necessary priors through appropriate expert feedback. Thus, in this work we present an active learning method that can be built upon existing deep learning solutions for unsupervised anomaly detection, so that outliers can be separated from normal data effectively. We introduce a new layer that can be easily attached to any deep learning model designed for unsupervised anomaly d...
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Machine Learning
Download from
43 references, page 1 of 3

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. Tensorflow: A system for large-scale machine learning. In OSDI, volume 16, pages 265-283, 2016.

Naoki Abe, Bianca Zadrozny, and John Langford. Outlier detection by active learning. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 504-509. ACM, 2006. [OpenAIRE]

Charu C Aggarwal. Outlier analysis. In Data mining, pages 237-263. Springer, 2015. [OpenAIRE]

Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, and Salah Rifai. Better mixing via deep representations. In International Conference on Machine Learning, pages 552-560, 2013.

Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. Lof: identifying densitybased local outliers. In ACM sigmod record, volume 29, pages 93-104. ACM, 2000. [OpenAIRE]

Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):15, 2009.

Yunqiang Chen, Xiang Sean Zhou, and Thomas S Huang. One-class svm for learning in image retrieval. In Image Processing, 2001. Proceedings. 2001 International Conference on, volume 1, pages 34-37. IEEE, 2001.

Shubhomoy Das, Weng-Keen Wong, Thomas Dietterich, Alan Fern, and Andrew Emmott. Incorporating expert feedback into active anomaly discovery. In International Conference on Data Mining (ICDM), pages 853-858. IEEE, 2016.

Anirban Dasgupta, John Hopcroft, Jon Kleinberg, and Mark Sandler. On learning mixtures of heavy-tailed distributions. In Foundations of Computer Science, 2005. FOCS 2005. 46th Annual IEEE Symposium on, pages 491-500. IEEE, 2005.

Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld. Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46:235-262, 2013. [OpenAIRE]

Frank E Grubbs. Procedures for detecting outlying observations in samples. Technometrics, 11(1): 1-21, 1969. [OpenAIRE]

Douglas M Hawkins. Identification of outliers, volume 11. Springer, 1980.

Victoria Hodge and Jim Austin. A survey of outlier detection methodologies. Artificial intelligence review, 22(2):85-126, 2004. [OpenAIRE]

Peter J Huber. Robust statistics. In International Encyclopedia of Statistical Science, pages 1248- 1251. Springer, 2011.

Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, and Yoshua Bengio. Fortified networks: Improving the robustness of deep networks by modeling the manifold of hidden representations. arXiv preprint arXiv:1804.02485, 2018. [OpenAIRE]

43 references, page 1 of 3
Abstract
Anomalies are intuitively easy for human experts to understand, but they are hard to define mathematically. Therefore, in order to have performance guarantees in unsupervised anomaly detection, priors need to be assumed on what the anomalies are. By contrast, active learning provides the necessary priors through appropriate expert feedback. Thus, in this work we present an active learning method that can be built upon existing deep learning solutions for unsupervised anomaly detection, so that outliers can be separated from normal data effectively. We introduce a new layer that can be easily attached to any deep learning model designed for unsupervised anomaly d...
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Machine Learning
Download from
43 references, page 1 of 3

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. Tensorflow: A system for large-scale machine learning. In OSDI, volume 16, pages 265-283, 2016.

Naoki Abe, Bianca Zadrozny, and John Langford. Outlier detection by active learning. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 504-509. ACM, 2006. [OpenAIRE]

Charu C Aggarwal. Outlier analysis. In Data mining, pages 237-263. Springer, 2015. [OpenAIRE]

Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, and Salah Rifai. Better mixing via deep representations. In International Conference on Machine Learning, pages 552-560, 2013.

Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. Lof: identifying densitybased local outliers. In ACM sigmod record, volume 29, pages 93-104. ACM, 2000. [OpenAIRE]

Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):15, 2009.

Yunqiang Chen, Xiang Sean Zhou, and Thomas S Huang. One-class svm for learning in image retrieval. In Image Processing, 2001. Proceedings. 2001 International Conference on, volume 1, pages 34-37. IEEE, 2001.

Shubhomoy Das, Weng-Keen Wong, Thomas Dietterich, Alan Fern, and Andrew Emmott. Incorporating expert feedback into active anomaly discovery. In International Conference on Data Mining (ICDM), pages 853-858. IEEE, 2016.

Anirban Dasgupta, John Hopcroft, Jon Kleinberg, and Mark Sandler. On learning mixtures of heavy-tailed distributions. In Foundations of Computer Science, 2005. FOCS 2005. 46th Annual IEEE Symposium on, pages 491-500. IEEE, 2005.

Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld. Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46:235-262, 2013. [OpenAIRE]

Frank E Grubbs. Procedures for detecting outlying observations in samples. Technometrics, 11(1): 1-21, 1969. [OpenAIRE]

Douglas M Hawkins. Identification of outliers, volume 11. Springer, 1980.

Victoria Hodge and Jim Austin. A survey of outlier detection methodologies. Artificial intelligence review, 22(2):85-126, 2004. [OpenAIRE]

Peter J Huber. Robust statistics. In International Encyclopedia of Statistical Science, pages 1248- 1251. Springer, 2011.

Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, and Yoshua Bengio. Fortified networks: Improving the robustness of deep networks by modeling the manifold of hidden representations. arXiv preprint arXiv:1804.02485, 2018. [OpenAIRE]

43 references, page 1 of 3
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue