Positive Macroscopic Approximation for Fast Attribute Reduction
Attribute reduction is one of the challenging problems facing the effective application of computational intelligence technology for artificial intelligence. Its task is to eliminate dispensable attributes and search for a feature subset that possesses the same classification capacity as that of the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/837281 |
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| _version_ | 1832566971893284864 |
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| author | Zheng-Cai Lu Zheng Qin Qiao Jing Lai-Xiang Shan |
| author_facet | Zheng-Cai Lu Zheng Qin Qiao Jing Lai-Xiang Shan |
| author_sort | Zheng-Cai Lu |
| collection | DOAJ |
| description | Attribute reduction is one of the challenging problems facing the effective application of computational intelligence technology for artificial intelligence. Its task is to eliminate dispensable attributes and search for a feature subset that possesses the same classification capacity as that of the original attribute set. To accomplish efficient attribute reduction, many heuristic search algorithms have been developed. Most of them are based on the model that the approximation of all the target concepts associated with a decision system is dividable into that of a single target concept represented by a pair of definable concepts known as lower and upper approximations. This paper proposes a novel model called macroscopic approximation, considering all the target concepts as an indivisible whole to be approximated by rough set boundary region derived from inconsistent tolerance blocks, as well as an efficient approximation framework called positive macroscopic approximation (PMA), addressing macroscopic approximations with respect to a series of attribute subsets. Based on PMA, a fast heuristic search algorithm for attribute reduction in incomplete decision systems is designed and achieves obviously better computational efficiency than other available algorithms, which is also demonstrated by the experimental results. |
| format | Article |
| id | doaj-art-9716c85590ff456387f984c250cf581e |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-9716c85590ff456387f984c250cf581e2025-02-03T01:02:45ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/837281837281Positive Macroscopic Approximation for Fast Attribute ReductionZheng-Cai Lu0Zheng Qin1Qiao Jing2Lai-Xiang Shan3Department of Computer Science & Technology, Tsinghua University, Beijing 100084, ChinaDepartment of Computer Science & Technology, Tsinghua University, Beijing 100084, ChinaDepartment of Computer Science & Technology, Tsinghua University, Beijing 100084, ChinaDepartment of Computer Science & Technology, Tsinghua University, Beijing 100084, ChinaAttribute reduction is one of the challenging problems facing the effective application of computational intelligence technology for artificial intelligence. Its task is to eliminate dispensable attributes and search for a feature subset that possesses the same classification capacity as that of the original attribute set. To accomplish efficient attribute reduction, many heuristic search algorithms have been developed. Most of them are based on the model that the approximation of all the target concepts associated with a decision system is dividable into that of a single target concept represented by a pair of definable concepts known as lower and upper approximations. This paper proposes a novel model called macroscopic approximation, considering all the target concepts as an indivisible whole to be approximated by rough set boundary region derived from inconsistent tolerance blocks, as well as an efficient approximation framework called positive macroscopic approximation (PMA), addressing macroscopic approximations with respect to a series of attribute subsets. Based on PMA, a fast heuristic search algorithm for attribute reduction in incomplete decision systems is designed and achieves obviously better computational efficiency than other available algorithms, which is also demonstrated by the experimental results.http://dx.doi.org/10.1155/2013/837281 |
| spellingShingle | Zheng-Cai Lu Zheng Qin Qiao Jing Lai-Xiang Shan Positive Macroscopic Approximation for Fast Attribute Reduction Journal of Applied Mathematics |
| title | Positive Macroscopic Approximation for Fast Attribute Reduction |
| title_full | Positive Macroscopic Approximation for Fast Attribute Reduction |
| title_fullStr | Positive Macroscopic Approximation for Fast Attribute Reduction |
| title_full_unstemmed | Positive Macroscopic Approximation for Fast Attribute Reduction |
| title_short | Positive Macroscopic Approximation for Fast Attribute Reduction |
| title_sort | positive macroscopic approximation for fast attribute reduction |
| url | http://dx.doi.org/10.1155/2013/837281 |
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