A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables
Joint Authors
Li, Hua
Li, Deyu
Zhai, Yanhui
Wang, Suge
Zhang, Jing
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-06
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Li, Hua& Li, Deyu& Zhai, Yanhui& Wang, Suge& Zhang, Jing. 2014. A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1049319
Modern Language Association (MLA)
Li, Hua…[et al.]. A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables. The Scientific World Journal No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1049319
American Medical Association (AMA)
Li, Hua& Li, Deyu& Zhai, Yanhui& Wang, Suge& Zhang, Jing. A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1049319
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049319