GA-fuzzy modeling and classification: complexity and performance

By Magne Setnes, Hans Roubos. IEEE transactions on fuzzy systems, 2000

499 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7e3c72ef0b1c90cd478335917fd9fc9f46dc3f48

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Citations in the corpus

Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases (2002, background): “This problem is usually solved by choosing conservatively large intervals for the parameter ranges (Lee and Takagi, 1993c; Lee and Takagi, 1996; Siarry and Guely, 1998; Setness and Roubos, 2000; de Sousa and Madrid, 2000; Wu and Yu, 2000).”
Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning (2007, methodology): “Some researchers tried to simultaneously perform the accuracy maximization and the complexity minimization in order to design fuzzy rule-based systems with high accuracy and high interpretability [2] [3] [4].”
Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, unknown): “Setnes and Roubos’ paper (IEEE TFS, 92 cites) [93].”
Tuning of the structure and parameters of neural network using an improved genetic algorithm (2001, methodology): “It has been applied in different areas such as fuzzy control [9]–[11], [15], path planning [12], greenhouse climate control [13], modeling and classification [14] etc.”
Ten years of genetic fuzzy systems: current framework and new trends (2001, methodology): “On the other hand, tuning the membership functions of a model working with fuzzy variables (scatter partitions) [5,63,120] is a particular instantiation of KB learning since the rules are completely de5ned by their own membership functions instead of referring to linguistic terms in the DB.”
Perspectives of fuzzy systems and control (2005, unknown): “The linguistic interpretability and transparency of fuzzy models constructed from data therefore became important research items in the literature [45,46,15,43,29].”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “Approaches in the field of fuzzy rule-based systems can be categorized as fuzzy grid-based [149], fuzzy clustering-based [313], fuzzy decision tree-based [157], and neuro-fuzzy methods [245,246].”