Abstract
A method of constructing a classifier that uses fuzzy reasoning is described in this paper. Rules for this classifier are obtained by means of algorithms relying on a tolerance rough sets model. Got rules are in so called sharp” form, a genetic algorithm is used for fuzzification of these rules. Presented results of experiments show that the proposed method allows getting a smaller rules set with similar (or better) classification abilities.
This research has been supported by the grant 5T12A00123 from Ministry of Scientific Research and Information Technology of the Republic of Poland.
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Sikora, M. (2005). Fuzzy Rules Generation Method for Classification Problems Using Rough Sets and Genetic Algorithms. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_40
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DOI: https://doi.org/10.1007/11548669_40
Publisher Name: Springer, Berlin, Heidelberg
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