Topic
Fuzzy associative matrix
About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.
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69 citations
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TL;DR: Experimental results show that better results are obtained from composite criteria of confidence and support measures than their individual use, and that genetic algorithm-based rule selection can improve the classification ability of extracted fuzzy rules by searching for good rule combinations.
Abstract: This paper compares heuristic criteria used for extracting a pre-specified number of fuzzy classification rules from numerical data. We examine the performance of each heuristic criterion through computational experiments on well-known test problems. Experimental results show that better results are obtained from composite criteria of confidence and support measures than their individual use. It is also shown that genetic algorithm-based rule selection can improve the classification ability of extracted fuzzy rules by searching for good rule combinations. This observation suggests the importance of taking into account the combinatorial effect of fuzzy rules (i.e., the interaction among them).
69 citations
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TL;DR: This work approximate parametric fuzzy numbers with polynomial parametric fuzziness, a version of fuzzy logic that is similar to but not the same as that in fuzzy mathematics.
69 citations
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01 Nov 2006TL;DR: A novel model to represent fuzzy knowledge is developed and when compared with other related models, the HLFPN model preserves several significant advantages.
Abstract: This correspondence presents a high-level fuzzy Petri net (HLFPN) model to represent the fuzzy production rules of a knowledge-based system, where a fuzzy production rule is the one that describes the fuzzy relation between the antecedent and the consequent. The HLFPN can be used to model fuzzy IF-THEN rules and IF-THEN-ELSE rules, where the fuzzy truth values of the propositions are restricted to [0, 1]. Based on the HLFPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. In this correspondence, a novel model to represent fuzzy knowledge is developed. When compared with other related models, the HLFPN model preserves several significant advantages. Finally, main results are presented in the form of eight properties and are supported by a comparison with other existing algorithms
69 citations
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TL;DR: In this paper, a heuristic algorithm for the identification of the 2-additive fuzzy measure, which is a particular type of k- Additive fuzzy measures, is proposed and can be used to reduce complexity of feature selection and classifier design.
69 citations