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The Scientific World Journal
Volume 2014, Article ID 359626, 7 pages
Research Article

A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables

1Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi 030006, China
2Department of Mathematics and Physics, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, China
3School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China

Received 18 June 2014; Accepted 17 July 2014; Published 6 August 2014

Academic Editor: Yunqiang Yin

Copyright © 2014 Hua Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable precision attribute reduct for multilabel data based on rough set theory, called -confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and discernibility matrix associated with -confidence reduct are also introduced, from which we can obtain the approach to knowledge reduction in multilabel decision tables.