Improving Multi-label Classifiers via Label Reduction with Association Rules

TitleImproving Multi-label Classifiers via Label Reduction with Association Rules
Publication TypeConference Paper
Year of Publication2012
AuthorsCharte, Francisco, Rivera Antonio J., del Jesus M. J., and Herrera F.
Conference Name7th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2012)
Pagination188–199
Date Published9
Conference LocationSalamanca (Spain)
ISBN Number978-3-642-28930-9
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.

Notes

TIN2008-06681-C06-02,TIC-3928

DOI10.1007/978-3-642-28931-6_18