Abstract
In this paper, we propose a fuzzy classifier system that can automatically generate linguistic rules from numerical data (i.e., from training patterns) for multi-dimensional pattern classification problems. Classifiers in our approach are linguistic rules such as“If x1 is small and x2 is large and x3is medium then Class 2 with CF=0.9” where CF is the grade of certainty of this rule. The grade of certainty of each linguistic rule is adjusted in each population by a reward and punishment scheme. A fitness value is also assigned to each linguistic rule, which is determined by its classification performance for training patterns. Our approach is illustrated by computer simulations on two-dimensional pattern classification problems. Learning ability for training patterns and generalization ability for test patterns of our approach are examined by several real-world pattern classification problems involving many features (i.e., many attributes).
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© 1996 Springer-Verlag Berlin Heidelberg
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Ishibuchi, H., Nakashima, T., Murata, T. (1996). A fuzzy classifier system that generates linguistic rules for pattern classification problems. In: Furuhashi, T., Uchikawa, Y. (eds) Fuzzy Logic, Neural Networks, and Evolutionary Computation. WWW 1995. Lecture Notes in Computer Science, vol 1152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61988-7_15
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DOI: https://doi.org/10.1007/3-540-61988-7_15
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