Access the full text.
Sign up today, get DeepDyve free for 14 days.
Erik Štrumbelj, I. Kononenko (2010)
An Efficient Explanation of Individual Classifications using Game TheoryJ. Mach. Learn. Res., 11
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, K. Müller (2009)
How to Explain Individual Classification DecisionsJ. Mach. Learn. Res., 11
Houtao Deng (2013)
Guided Random Forest in the RRF PackageArXiv, abs/1306.0237
X. Nguyen, Jeffrey Chan, J. Bailey, C. Leckie, K. Ramamohanarao, J. Pei (2015)
Scalable Outlying-Inlying Aspects Discovery via Feature Ranking
Xuan-Hong Dang, I. Assent, R. Ng, A. Zimek, Erich Schubert (2014)
Discriminative features for identifying and interpreting outliers2014 IEEE 30th International Conference on Data Engineering
Seth Hettich, S. D. Bay (1999)
The UCI KDD ArchiveDepartment of Information and Computer Science, University of California, Irvine, CA. Retrieved from http://kdd.ics.uci.edu.
(1999)
The UCI KDD archive [http://kdd. ics. uci. edu]. irvine, ca: University of california
Barbora Micenková, R. Ng, Xuan-Hong Dang, I. Assent (2013)
Explaining Outliers by Subspace Separability2013 IEEE 13th International Conference on Data Mining
Xuan Hong Dang, Ira Assent, Raymond T. Ng, Arthur Zimek, Erich Schubert (2014)
Discriminative features for identifying and interpreting outliersProceedings of the IEEE 30th International Conference on Data Engineering (ICDE’14)
Lei Duan, Guanting Tang, J. Pei, J. Bailey, Akiko Campbell, Changjie Tang (2015)
Mining outlying aspects on numeric dataData Mining and Knowledge Discovery, 29
Barbora Micenková, Raymond T. Ng, Xuan-Hong Dang, Ira Assent (2013)
Explaining outliers by subspace separabilityProceedings of the IEEE 13th International Conference on Data Mining (ICDM’13)
Andrew Emmott, S. Das, Thomas Dietterich, Alan Fern, Weng-Keen Wong (2013)
Systematic construction of anomaly detection benchmarks from real dataArXiv, abs/1503.01158
Andreas Krause, D. Golovin (2014)
Submodular Function Maximization
M. Delgado, E. Cernadas, S. Barro, D. Amorim (2014)
Do we need hundreds of classifiers to solve real world classification problems?J. Mach. Learn. Res., 15
Xuan-Hong Dang, Barbora Micenková, I. Assent, R. Ng (2013)
Local Outlier Detection with Interpretation
M. Robnik-Sikonja, I. Kononenko (2008)
Explaining Classifications For Individual InstancesIEEE Transactions on Knowledge and Data Engineering, 20
Erik Štrumbelj, I. Kononenko (2014)
Explaining prediction models and individual predictions with feature contributionsKnowledge and Information Systems, 41
Radford Neal (2006)
Pattern Recognition and Machine LearningPattern Recognition and Machine Learning
In many applications, an anomaly detection system presents the most anomalous data instance to a human analyst, who then must determine whether the instance is truly of interest (e.g., a threat in a security setting). Unfortunately, most anomaly detectors provide no explanation about why an instance was considered anomalous, leaving the analyst with no guidance about where to begin the investigation. To address this issue, we study the problems of computing and evaluating sequential feature explanations (SFEs) for anomaly detectors. An SFE of an anomaly is a sequence of features, which are presented to the analyst one at a time (in order) until the information contained in the highlighted features is enough for the analyst to make a confident judgement about the anomaly. Since analyst effort is related to the amount of information that they consider in an investigation, an explanation’s quality is related to the number of features that must be revealed to attain confidence. In this article, we first formulate the problem of optimizing SFEs for a particular density-based anomaly detector. We then present both greedy algorithms and an optimal algorithm, based on branch-and-bound search, for optimizing SFEs. Finally, we provide a large scale quantitative evaluation of these algorithms using a novel framework for evaluating explanations. The results show that our algorithms are quite effective and that our best greedy algorithm is competitive with optimal solutions.
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Jan 9, 2019
Keywords: Anomaly detection
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.