Abstract

Rare category detection refers to the problem of identifying the initial examples from under-represented minority classes in an imbalanced data set. This problem becomes more challenging in many real applications where the data comes from multiple views, and some views may be irrelevant for distinguishing between majority and minority classes, such as synthetic ID detection and insider threat detection. Existing techniques for rare category detection are not best suited for such applications, as they mainly focus on data with a single view. To address the problem of multi-view rare category detection, in this paper, we propose a novel framework named MUVIR. It builds upon existing techniques for rare category detection with each single view, and exploits the relationship among multiple views to estimate the overall probability of each example belonging to the minority class. In particular, we study multiple special cases of the framework with respect to their working conditions, and analyze the performance of MUVIR in the presence of irrelevant views. For problems where the exact priors of the minority classes are unknown, we generalize the MUVIR algorithm to work with only an upper bound on the priors. Experimental results on both synthetic and real data sets demonstrate the effectiveness of the proposed framework, especially in the presence of irrelevant views.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Pages4098-4104
Number of pages7
Volume2015-January
ISBN (Print)9781577357384
StatePublished - 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: Jul 25 2015Jul 31 2015

Other

Other24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CountryArgentina
CityBuenos Aires
Period7/25/157/31/15

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Zhou, D., He, J., Candan, K., & Davulcu, H. (2015). MUVIR: Multi-view rare category detection. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2015-January, pp. 4098-4104). International Joint Conferences on Artificial Intelligence.

MUVIR : Multi-view rare category detection. / Zhou, Dawei; He, Jingrui; Candan, Kasim; Davulcu, Hasan.

IJCAI International Joint Conference on Artificial Intelligence. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. p. 4098-4104.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhou, D, He, J, Candan, K & Davulcu, H 2015, MUVIR: Multi-view rare category detection. in IJCAI International Joint Conference on Artificial Intelligence. vol. 2015-January, International Joint Conferences on Artificial Intelligence, pp. 4098-4104, 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, 7/25/15.
Zhou D, He J, Candan K, Davulcu H. MUVIR: Multi-view rare category detection. In IJCAI International Joint Conference on Artificial Intelligence. Vol. 2015-January. International Joint Conferences on Artificial Intelligence. 2015. p. 4098-4104
Zhou, Dawei ; He, Jingrui ; Candan, Kasim ; Davulcu, Hasan. / MUVIR : Multi-view rare category detection. IJCAI International Joint Conference on Artificial Intelligence. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. pp. 4098-4104
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