Multi-label structure learning with ising model selection

André R. Gonçalves, Fernando J. Von Zuben, Arindam Banerjee

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

6 Scopus citations

Abstract

A common way of attacking multi-label classification problems is by splitting it into a set of binary classification problems, then solving each problem independently using traditional single-label methods. Nevertheless, by learning classifiers separately the information about the relationship between labels tends to be neglected. Built on recent advances in structure learning in Ising Markov Random Fields (I-MRF), we propose a multi-label classification algorithm that explicitly estimate and incorporate label dependence into the classifiers learning process by means of a sparse convex multitask learning formulation. Extensive experiments considering several existing multi-label algorithms indicate that the proposed method, while conceptually simple, outperforms the contenders in several datasets and performance metrics. Besides that, the conditional dependence graph encoded in the I-MRF provides a useful information that can be used in a posterior investigation regarding the reasons behind the relationship between labels.

Original languageEnglish (US)
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
EditorsMichael Wooldridge, Qiang Yang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3525-3531
Number of pages7
ISBN (Electronic)9781577357384
StatePublished - 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: Jul 25 2015Jul 31 2015

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2015-January
ISSN (Print)1045-0823

Other

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

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