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      A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables

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          Abstract

          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 δ-confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and discernibility matrix associated with δ-confidence reduct are also introduced, from which we can obtain the approach to knowledge reduction in multilabel decision tables.

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          Most cited references 23

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          LERS-A System for Learning from Examples Based on Rough Sets

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            A kernel method for multi-labelled classification

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              Comparative study of alternative types of knowledge reduction in inconsistent systems

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                Author and article information

                Journal
                ScientificWorldJournal
                ScientificWorldJournal
                TSWJ
                The Scientific World Journal
                Hindawi Publishing Corporation
                2356-6140
                1537-744X
                2014
                6 August 2014
                : 2014
                Affiliations
                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
                Author notes

                Academic Editor: Yunqiang Yin

                Article
                10.1155/2014/359626
                4142157
                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.

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