A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables
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 applie...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/359626 |
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| _version_ | 1832568350553669632 |
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| author | Hua Li Deyu Li Yanhui Zhai Suge Wang Jing Zhang |
| author_facet | Hua Li Deyu Li Yanhui Zhai Suge Wang Jing Zhang |
| author_sort | Hua Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-46b9a5d170ce403ca9e4752a076e2171 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-46b9a5d170ce403ca9e4752a076e21712025-02-03T00:59:14ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/359626359626A Variable Precision Attribute Reduction Approach in Multilabel Decision TablesHua Li0Deyu Li1Yanhui Zhai2Suge Wang3Jing Zhang4Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi 030006, ChinaKey Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi 030006, ChinaSchool of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, ChinaSchool of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, ChinaSchool of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, ChinaOwing 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.http://dx.doi.org/10.1155/2014/359626 |
| spellingShingle | Hua Li Deyu Li Yanhui Zhai Suge Wang Jing Zhang A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables The Scientific World Journal |
| title | A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables |
| title_full | A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables |
| title_fullStr | A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables |
| title_full_unstemmed | A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables |
| title_short | A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables |
| title_sort | variable precision attribute reduction approach in multilabel decision tables |
| url | http://dx.doi.org/10.1155/2014/359626 |
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