Ensemble and Quick Strategy for Searching Reduct: A Hybrid Mechanism
Attribute reduction is commonly referred to as the key topic in researching rough set. Concerning the strategies for searching reduct, though various heuristics based forward greedy searchings have been developed, most of them were designed for pursuing one and only one characteristic which is close...
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| Format: | Article |
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MDPI AG
2021-01-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/1/25 |
| _version_ | 1827602577420713984 |
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| author | Wangwang Yan Yan Chen Jinlong Shi Hualong Yu Xibei Yang |
| author_facet | Wangwang Yan Yan Chen Jinlong Shi Hualong Yu Xibei Yang |
| author_sort | Wangwang Yan |
| collection | DOAJ |
| description | Attribute reduction is commonly referred to as the key topic in researching rough set. Concerning the strategies for searching reduct, though various heuristics based forward greedy searchings have been developed, most of them were designed for pursuing one and only one characteristic which is closely related to the performance of reduct. Nevertheless, it is frequently expected that a justifiable searching should explicitly involves three main characteristics: (1) the process of obtaining reduct with low time consumption; (2) generate reduct with high stability; (3) acquire reduct with competent classification ability. To fill such gap, a hybrid based searching mechanism is designed, which takes the above characteristics into account. Such a mechanism not only adopts multiple fitness functions to evaluate the candidate attributes, but also queries the distance between attributes for determining whether two or more attributes can be added into the reduct simultaneously. The former may be useful in deriving reduct with higher stability and competent classification ability, and the latter may contribute to the lower time consumption of deriving reduct. By comparing with 5 state-of-the-art algorithms for searching reduct, the experimental results over 20 UCI data sets demonstrate the effectiveness of our new mechanism. This study suggests a new trend of attribute reduction for achieving a balance among various characteristics. |
| first_indexed | 2024-03-09T05:22:25Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T05:22:25Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-a64da475907341a6941545365018436b2023-12-03T12:39:57ZengMDPI AGInformation2078-24892021-01-011212510.3390/info12010025Ensemble and Quick Strategy for Searching Reduct: A Hybrid MechanismWangwang Yan0Yan Chen1Jinlong Shi2Hualong Yu3Xibei Yang4School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaAttribute reduction is commonly referred to as the key topic in researching rough set. Concerning the strategies for searching reduct, though various heuristics based forward greedy searchings have been developed, most of them were designed for pursuing one and only one characteristic which is closely related to the performance of reduct. Nevertheless, it is frequently expected that a justifiable searching should explicitly involves three main characteristics: (1) the process of obtaining reduct with low time consumption; (2) generate reduct with high stability; (3) acquire reduct with competent classification ability. To fill such gap, a hybrid based searching mechanism is designed, which takes the above characteristics into account. Such a mechanism not only adopts multiple fitness functions to evaluate the candidate attributes, but also queries the distance between attributes for determining whether two or more attributes can be added into the reduct simultaneously. The former may be useful in deriving reduct with higher stability and competent classification ability, and the latter may contribute to the lower time consumption of deriving reduct. By comparing with 5 state-of-the-art algorithms for searching reduct, the experimental results over 20 UCI data sets demonstrate the effectiveness of our new mechanism. This study suggests a new trend of attribute reduction for achieving a balance among various characteristics.https://www.mdpi.com/2078-2489/12/1/25attribute reductionensemble selectorrough setstability |
| spellingShingle | Wangwang Yan Yan Chen Jinlong Shi Hualong Yu Xibei Yang Ensemble and Quick Strategy for Searching Reduct: A Hybrid Mechanism Information attribute reduction ensemble selector rough set stability |
| title | Ensemble and Quick Strategy for Searching Reduct: A Hybrid Mechanism |
| title_full | Ensemble and Quick Strategy for Searching Reduct: A Hybrid Mechanism |
| title_fullStr | Ensemble and Quick Strategy for Searching Reduct: A Hybrid Mechanism |
| title_full_unstemmed | Ensemble and Quick Strategy for Searching Reduct: A Hybrid Mechanism |
| title_short | Ensemble and Quick Strategy for Searching Reduct: A Hybrid Mechanism |
| title_sort | ensemble and quick strategy for searching reduct a hybrid mechanism |
| topic | attribute reduction ensemble selector rough set stability |
| url | https://www.mdpi.com/2078-2489/12/1/25 |
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