Parallel attribute reduction in dominance-based neighborhood rough set
The amount of data collected from different real-world applications is increasing rapidly. When the volume of data is too large to be loaded to memory, it may be impossible to analyze it using a single computer. Although efforts have been taken to manage big data by using a single computer, the prob...
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| Veröffentlicht in: | Information sciences Jg. 373; S. 351 - 368 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Inc
10.12.2016
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | The amount of data collected from different real-world applications is increasing rapidly. When the volume of data is too large to be loaded to memory, it may be impossible to analyze it using a single computer. Although efforts have been taken to manage big data by using a single computer, the problem may not be solved in an acceptable time frame, making parallel computing an indispensable way to handle big data. In this paper, we investigate approaches to attribute reduction in parallel using dominance-based neighborhood rough sets (DNRS), which take into consideration the partial orders among numerical and categorical attribute values, and can be utilized in a multicriteria decision-making method. We first present some properties of attribute reduction in DNRS, and then investigate principles of parallel attribute reduction in DNRS. Parallelization on different components of attribute reduction are explored in detail. Furthermore, parallel attribute reduction algorithms in DNRS are proposed. Experimental results on UCI data and big data show that the proposed parallel algorithm is both effective and efficient. |
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| AbstractList | The amount of data collected from different real-world applications is increasing rapidly. When the volume of data is too large to be loaded to memory, it may be impossible to analyze it using a single computer. Although efforts have been taken to manage big data by using a single computer, the problem may not be solved in an acceptable time frame, making parallel computing an indispensable way to handle big data. In this paper, we investigate approaches to attribute reduction in parallel using dominance-based neighborhood rough sets (DNRS), which take into consideration the partial orders among numerical and categorical attribute values, and can be utilized in a multicriteria decision-making method. We first present some properties of attribute reduction in DNRS, and then investigate principles of parallel attribute reduction in DNRS. Parallelization on different components of attribute reduction are explored in detail. Furthermore, parallel attribute reduction algorithms in DNRS are proposed. Experimental results on UCI data and big data show that the proposed parallel algorithm is both effective and efficient. |
| Author | Li, Tianrui Cai, Yong Fujita, Hamido Luo, Chuan Chen, Hongmei |
| Author_xml | – sequence: 1 givenname: Hongmei surname: Chen fullname: Chen, Hongmei email: hmchen@swjtu.edu.cn organization: School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China – sequence: 2 givenname: Tianrui surname: Li fullname: Li, Tianrui email: trli@swjtu.edu.cn organization: School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China – sequence: 3 givenname: Yong surname: Cai fullname: Cai, Yong email: yongcai@my.swjtu.edu.cn organization: School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China – sequence: 4 givenname: Chuan surname: Luo fullname: Luo, Chuan email: cluo@scu.edu.cn organization: College of Computer Science, Sichuan University, Chengdu 610065, China – sequence: 5 givenname: Hamido orcidid: 0000-0001-5256-210X surname: Fujita fullname: Fujita, Hamido email: issam@iwate-pu.ac.jp organization: School of Intelligent Software Systems, Iwate Prefectural University, 152-52 Sugo,Takizawa-shi 020-0693, Japan |
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| Keywords | Parallel algorithm Big data Rough sets Attribute reduction |
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| Title | Parallel attribute reduction in dominance-based neighborhood rough set |
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