Using Rough Sets Theory and Minimum Description Length Principle to Improve a β-TSK Fuzzy Revision Method for CBR Systems
- Cite this paper as:
- Fdez-Riverola F., Díaz F., Corchado J.M. (2004) Using Rough Sets Theory and Minimum Description Length Principle to Improve a β-TSK Fuzzy Revision Method for CBR Systems. In: Bazzan A.L.C., Labidi S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science, vol 3171. Springer, Berlin, Heidelberg
This paper examines a fuzzy logic based method that automates the review stage of a 4-step Case Based Reasoning system and aids in the process of obtaining an accurate solution. The proposed method has been derived as an extension of the Sugeno Fuzzy model, and evaluates different solutions by reviewing their score in an unsupervised mode. In addition, this paper proposes an improvement of the original fuzzy revision method based on the reduction of the original set of attributes that define a case. This task is performed by a feature subset selection algorithm based on the Rough Set theory and the minimum description length principle.
KeywordsCBR TSK fuzzy models rough sets minimum description length automated revision stage
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