Improving multi-label classifiers via label reduction with association rules

Authors:
Francisco Charte;Antonio Rivera;María José del Jesus;Francisco Herrera
Affiliations:
Dep. of Computer Science, University of Jaén, Jaén, Spain;Dep. of Computer Science, University of Jaén, Jaén, Spain;Dep. of Computer Science, University of Jaén, Jaén, Spain;Dep. of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Year:
2012

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Abstract

Multi-label classification is a generalization of well known problems, such as binary or multi-class classification, in a way that each processed instance is associated not with a class (label) but with a subset of these. In recent years different techniques have appeared which, through the transformation of the data or the adaptation of classic algorithms, aim to provide a solution to this relatively recent type of classification problem. This paper presents a new transformation technique for multi-label classification based on the use of association rules aimed at the reduction of the label space to deal with this problem.