1999 | OriginalPaper | Chapter
Generating Linguistic Fuzzy Rules for Pattern Classification with Genetic Algorithms
Authors : N. Xiong, L. Litz
Published in: Principles of Data Mining and Knowledge Discovery
Publisher: Springer Berlin Heidelberg
Included in: Professional Book Archive
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This paper presents a new genetic-based approach to automatically extracting classification knowledge from numerical data by means of premise learning. A genetic algorithm is utilized to search for premise structure in combination with parameters of membership functions of input fuzzy sets to yield optimal conditions of classification rules. The consequence under a specific condition is determined by choosing from all possible candidates the class which lead to a maximal truth value of the rule. The major advantage of our work is that a parsimonious knowledge base with a low number of classification rules is made possible. The effectiveness of the proposed method is demonstrated by the simulation results on the Iris data.