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

By Hisao Ishibuchi, Takashi Yamamoto. Fuzzy Sets Syst., 2004

426 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/82b3e4d149744b5c9e564dcb5a9ebaa44a494d73

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In Artificial Intelligence

Citations in the corpus

Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures (2011, methodology): “[42] apply an improved MOEA, the multi-objective genetic local search [34] (MOGLS) for classification problems, considering the same approach as in [37] with three objectives: Maximizing the number of correctly classified training patterns, minimizing the number of fuzzy rules, and minimizing the to”
Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning (2007, methodology): “This formulation was further extended to three-objective rule selection in [15,16] as follows: Maximize f 1ðSÞ; minimize f 2ðSÞ; and minimize f 3ðSÞ; ð3Þ”
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, neutral): “, a hybrid algorithm of EMO and local search) was considered with the latter three-objective genetic rule selection in [99].”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “These include feature construction or selection [347], training example subset selection [93], and rulebase optimization [152,243].”
A Multi-Facet Survey on Memetic Computation (2011, background): “One of the earlier work in MOMA [76] proposed a local search operator that perturbs the solutions in a randomly chosen non-dominated improving direction of the objectives, as a means of enhancing the rate of convergence to the Pareto front.”
Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, neutral): “(a) Multiobjective genetic rule selection [62, 66].”
A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets (2008, methodology): “We will also include in this analysis a linguistic FRBCS generated by a common approach [24–26], and a new one, the E-Algorithm [47], which is an extension of the previous method to generate an RB adapted to imbalanced data-sets.”
Multiobjective Optimization in Bioinformatics and Computational Biology (2007, background): “The benefits of integrating model complexity as a second and/ or third objective have been previously demonstrated with respect to fuzzy rule mining [ 35 ], learning classifier systems [36], decision trees [37], support vector machines [38], genetic programming (GP) [39], [40], [41], and artificial ”
Memetic Computation—Past, Present & Future [Research Frontier] (2010, background): “…magnet synchronous motor [23], datamining , machine learning and artificial neural networks [24], dynamic optimization problems [23] [25], computationally expensive environments [20] [26], robust design [26], fuzzy modeling [27] [28], and multi-objective memetic search [29] [30] [31] [32] [33].”
Learning Complexity-Bounded Rule-Based Classifiers by Combining. Association Analysis and Genetic Algorithms (2005, methodology): “Moreover, we currently think about replacing association analysis by alterna- tive methods for generating candidate rules such as, e.g., modified versions of standard rule induction algorithms.”