An enhancement for heuristic attribute reduction algorithm in rough set |
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| Affiliation: | 1. Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China;2. Department of Automobile Engineering, College of Mechanical Engineering, Chongqing University, 174 Shazheng Street, Chongqing 400044, PR China;1. Department of Engineering, University of Almería, 04120 Almería, Spain;2. Department of Informatics, University of Almería, 04120 Almería, Spain;3. Facultad de Ingenería, Universidad Veracruzana, Campus Coatzacoalcos, Coatzacoalcos, Mexico;1. Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2950, Valparaíso, Chile;2. Universidad Finis Terrae, Av. Pedro de Valdivia 1509, Santiago, Chile;3. Universidad de Playa Ancha, Av. Leopoldo Carvallo 270, Valparaíso, Chile;4. Universidad Autónoma de Chile, Pedro de Valdivia 641, Santiago, Chile;5. CNRS, LINA, University of Nantes, 2 rue de la Houssinière, Nantes, France;6. Escuela de Ingeniería Industrial, Universidad Diego Portales, Manuel Rodríguez Sur 415, Santiago, Chile;1. Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, No. 129, Sec. 3, Sanmin Rd., Taichung, Taiwan, ROC;2. Department of Electrical Engineering, National Chung Hsing University, No. 250 Kuo Kuang Rd., Taichung, Taiwan, ROC;1. Department of Electrical and Computer Engineering, University of Macau, Avenida Padre Tomas Pereira, Taipa, Macau;2. Department of Computer Science, Huizhou University, Huizhou 516007, China;1. Department of Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil;2. Department of Computer Science, Federal University of Ouro Preto, Ouro Preto, Brazil;3. IT Center, Federal University of Ouro Preto, Ouro Preto, Brazil |
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| Abstract: | Attribute reduction is one of the most important issues in the research of rough set theory. Numerous significance measure based heuristic attribute reduction algorithms have been presented to achieve the optimal reduct. However, how to handle the situation that multiple attributes have equally largest significances is still largely unknown. In this regard, an enhancement for heuristic attribute reduction (EHAR) in rough set is proposed. In some rounds of the process of adding attributes, those that have the same largest significance are not randomly selected, but build attribute combinations and compare their significances. Then the most significant combination rather than a randomly selected single attribute is added into the reduct. With the application of EHAR, two representative heuristic attribute reduction algorithms are improved. Several experiments are used to illustrate the proposed EHAR. The experimental results show that the enhanced algorithms with EHAR have a superior performance in achieving the optimal reduct. |
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| Keywords: | Rough set Heuristic Attribute reduction Enhancement for heuristic attribute reduction |
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