Please use this identifier to cite or link to this item:
Title: Information gain feature selection for multi-label classification.
Authors: Pereira, Rafael Barros
Carvalho, Alexandre Plastino de
Zadrozny, Bianca
Merschmann, Luiz Henrique de Campos
Keywords: Classification
Data mining
Feature selection
Multi label classification
Issue Date: 2015
Citation: PEREIRA, R. B. et al. Information gain feature selection for multi-label classification. Journal of Information and Data Management - JIDM, v. 6, p. 48-58, 2015. Disponível em: <>. Acesso em: 07 ago. 2016.
Abstract: In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research in multi-label classification. And, more specifically, many feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. However, most methods proposed for this task rely on the transformation of the multi-label data set into a single-label one. In this work we have chosen one of the most wellknown measures for feature selection – Information Gain – and we have evaluated it along with common transformation techniques for the multi-label classification. We have also adapted the information gain feature selection technique to handle multi-label data directly. Our goal is to perform a thorough investigation of the performance of multi-label feature selection techniques using the information gain concept and report how it varies when coupled with different multi-label classifiers and data sets from different domains.
ISSN: 2178-7107
metadata.dc.rights.license: Permission to copy without fee all or part of the material printed in JIDM is granted provided that the copies are not made or distributed for commercial advantage, and that notice is given that copying is by permission of the Sociedade Brasileira de Computação. Fonte: o próprio artigo.
Appears in Collections:DECOM - Artigos publicados em periódicos

Files in This Item:
File Description SizeFormat 
ARTIGO_InformationGainFeature.pdf429,38 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.