Learning Fuzzy Rules from Fuzzy Decision Trees
نویسندگان
چکیده
Classification rules are an important tool for discovering knowledge from databases. Integrating fuzzy logic algorithms into databases allows us to reduce uncertainty which is connected with data in databases and to increase discovered knowledge’s accuracy. In this paper, we analyze some possible variants of making classification rules from a given fuzzy decision based on cumulative information. We compare their classification accuracy with the accuracy which is reached by statistical methods and other fuzzy classification rules.
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