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Li H, Li D, Zhai Y, et al. A variable precision attribute reduction approach in multilabel decision tables. ScientificWorldJournal. 2014;2014:359626doi: 10.1155/2014/359626.
Li, H., Li, D., Zhai, Y., Wang, S., & Zhang, J. (2014). A variable precision attribute reduction approach in multilabel decision tables. TheScientificWorldJournal, 2014359626. https://doi.org/10.1155/2014/359626
Li, Hua, et al. "A variable precision attribute reduction approach in multilabel decision tables." TheScientificWorldJournal vol. 2014 (2014): 359626. doi: https://doi.org/10.1155/2014/359626
Li H, Li D, Zhai Y, Wang S, Zhang J. A variable precision attribute reduction approach in multilabel decision tables. ScientificWorldJournal. 2014;2014:359626. doi: 10.1155/2014/359626. Epub 2014 Aug 06. PMID: 25170521; PMCID: PMC4142157.
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ScientificWorldJournal. 2014;2014:359626. doi: 10.1155/2014/359626. Epub 2014 Aug 06.

A variable precision attribute reduction approach in multilabel decision tables.

TheScientificWorldJournal

Hua Li, Deyu Li, Yanhui Zhai, Suge Wang, Jing Zhang

Affiliations

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

PMID: 25170521 PMCID: PMC4142157 DOI: 10.1155/2014/359626

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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