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Rough set feature extraction by remarkable degrees with real world decision-making problems

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Abstract

In this paper, a novel approach for simplifying the obtained if–then rules from decision table by rough set based methods is proposed. This approach can extract main features of objects in different decision classes by remarkable degrees. Using the proposed method, two real world decision-making problems are studied. The first one is for analyzing the main features of Japanese industries. The second is for anatomizing the life situations of senior citizens in Japan. The analysis results show that the proposed method is powerful for piecing out the main features of decision classes in the decision table which has many attributes and objects. The obtained main features can provide deep insight into the situations of objects and are useful for making a decision in the real world.

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Authors and Affiliations

  1. Faculty of Business Administration, Yokohama National University, 79-4 Tokiwadai, Hodogaya-Ku, Yokohama, 240-8501, Japan

    Peijun Guo

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  1. Peijun Guo
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Correspondence to Peijun Guo.

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Guo, P. Rough set feature extraction by remarkable degrees with real world decision-making problems. Soft Comput 14, 1265–1275 (2010). https://doi.org/10.1007/s00500-009-0494-1

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