Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining

Ishibuchi, Hisao; Yamamoto, Takashi

doi:10.1016/s0165-0114(03)00114-3

Cited by 397 publications

(249 citation statements)

References 25 publications

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“…Romao [9] uses genetic algorithms to discover fuzzy rules. Ishibuchi [5] also uses genetic algorithms to construct fuzzy rules, but focusing in evolving classifiers instead of single rules.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules

Giusti

Batista

Prati

2008

2008 Eighth International Conference on Hybrid Intelligent Systems

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Most symbolic classifiers aim at building sets of rules with good coverage and precision. While this is suitable for most applications, they tend to neglect other desirable properties, such as the ability to induce novel knowledge or to show new points of view of well-established concepts. An approach to overcome these limitations involves using a multi-objective evolutionary algorithm to build knowledge rules with specific properties specified by the user. In this paper, we report a research work that combined evolutionary algorithms and ranking composition methods for multi-objective optimization. In this approach, candidate solutions are built, evaluated and ranked according to their performance in each individual objective. Then rankings are composed into a single ranking which reflects the candidate solutions' ability to solve the multi-objective problem considering all objectives simultaneously. We investigate the behavior of 5 ranking composition methods. These methods are compared and we conclude that all of the studied ranking composition methods provide good balance of objectives. Moreover, for the 11 datasets analyzed, we conclude condorcet is the only method which performs statistically better than other methods.

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Section: Related Workmentioning

confidence: 99%

Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules

Giusti

Batista

Prati

2008

2008 Eighth International Conference on Hybrid Intelligent Systems

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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%

“…In our former studies (Ishibuchi, Yamamoto and Nakashima (2001), Ishibuchi and Yamamoto (2003b)), we used the confidence, the support and their product as rule selection criteria for extracting a pre-specified number of fuzzy rules from numerical data. Experimental results in those studies showed that the product criterion of the confidence and the support outperformed their individual use.…”

Section: Rule Selection Criteriamentioning

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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“…Several authors have proposed a genetic algorithm for fuzzy neural parameters optimization to adjust the control points of membership functions or to tune the weightings [9][10][11][12][13][14]. The pioneer was Karr [9] , who used GAs to adjust membership functions.…”

Section: Introductionmentioning

confidence: 99%

“…Ishibuchi et al [10] proposed a genetic-based method for selecting a small number of significant fuzzy rules to construct a compact fuzzy classification system with high classification power. Ishibuchi and Yamamoto farther developed this idea by using mult-objective genetic local search algorithms in [13]. Wang et al [11] have pro- posed a simplified genetic algorithm to adjust both control points of B-spline membership functions and weights of fuzzy-neural networks.…”

Section: Introductionmentioning

confidence: 99%

GA-Based Learning Algorithms to Identify Fuzzy Rules for Fuzzy Neural Networks

Almejalli

Dahal

Hossain

2007

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007)

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Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining

Cited by 397 publications

References 25 publications

Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules

Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules

Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

GA-Based Learning Algorithms to Identify Fuzzy Rules for Fuzzy Neural Networks

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