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.
CITATION STYLE
Xiong, N., & Litz, L. (1999). Generating linguistic fuzzy rules for pattern classification with genetic algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1704, pp. 574–579). Springer Verlag. https://doi.org/10.1007/978-3-540-48247-5_76
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