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http://dx.doi.org/10.5391/IJFIS.2004.4.2.205

Efficient Extraction of Hierarchically Structured Rules Using Rough Sets  

Lee, Chul-Heui (Dept. of Electrical and Computer Engineering, Kangwon National University)
Seo, Seon-Hak (Dept. of Electrical and Computer Engineering, Kangwon National University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.4, no.2, 2004 , pp. 205-210 More about this Journal
Abstract
This paper deals with rule extraction from data using rough set theory. We construct the rule base in a hierarchical granulation structure by applying core as a classification criteria at each level. When more than one core exist, the coverage is used for the selection of an appropriate one among them to increase the classification rate and accuracy. In Addition, a probabilistic approach is suggested so that the partially useful information included in inconsistent data can be contributed to knowledge reduction in order to decrease the effect of the uncertainty or vagueness of data. As a result, the proposed method yields more proper and efficient rule base in compatability and size. The simulation result shows that it gives a good performance in spite of very simple rules and short conditionals.
Keywords
rule extraction; inconsistent data; hierarchical granulation structure; coverage; probabilistic approach; rough set;
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