Distributed representation of fuzzy rules and its application to pattern classification

Ishibuchi, Hisao; Nozaki, Kengo; Tanaka, Hideo

doi:10.1016/0165-0114(92)90032-y

Cited by 465 publications

(187 citation statements)

References 6 publications

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“…The definition in (15) has been used in many fuzzy rule-based classification systems in our former studies (e.g., Ishibuchi, Nakashima and Murata (1999), ) since Ishibuchi, Nozaki and Tanaka (1992). On the other hand, the definition in (16) has been used in some recent studies (e.g., Ishibuchi and Yamamoto (2003b)).…”

Section: Fuzzy Rules For Classification Problemsmentioning

confidence: 99%

Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

Ishibuchi

Yamamoto

2004

Fuzzy Optimization and Decision Making

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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).

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Section: Fuzzy Rules For Classification Problemsmentioning

confidence: 99%

Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

Ishibuchi

Yamamoto

2004

Fuzzy Optimization and Decision Making

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“…In particular, it assures the consistency of the rule base [18]. If no information is processed that is the input space is not covered by rule set; the output gives an "unknown defect".The chosen classifier is based on Ishibuchi's algorithm which provides an automatic rule generation step [19]. There are many methods, which automatically provide fuzzy rules according to data set such as a genetic algorithm [20], but the Ishibushi's algorithm is quite simple and gives better results [10].…”

Section: Fuzzy Rule Generationmentioning

confidence: 99%

Fuzzy Rule Iterative Feature Selection (FRIFS) with Respect to the Choquet Integral Apply to Fabric Defect Recognition

Schmitt

Bombardier

Wendling

2008

Advanced Concepts for Intelligent Vision Systems

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“…This approach uses a reduced set of features extracted from the original ones by the principal component analysis. After that, a fuzzy rule learning process is carried out following the method proposed in [9] which divides the pattern space in several fuzzy subspaces, learning a rule for each one. Finally, a modified threshold accepting algorithm [17] is used to build a compact rule subset with a high classification accuracy, from rule set obtained in the previous stage.…”

Section: Experimental Studymentioning

confidence: 99%

A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems

Berlanga

Jesús

Gacto

et al. 2006

Artificial Intelligence and Soft Computing – ICAISC 2006

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Abstract. In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where disjunctive normal form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy rules, which presents a good classification performance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the population. The good results obtained with several classification problems support our proposal.

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Distributed representation of fuzzy rules and its application to pattern classification

Cited by 465 publications

References 6 publications

Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

Fuzzy Rule Iterative Feature Selection (FRIFS) with Respect to the Choquet Integral Apply to Fabric Defect Recognition

A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems

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