Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction

DOI10.1016/J.FSS.2004.07.013zbMath1071.68550OpenAlexW2118030936MaRDI QIDQ703362

Sam Kwong, Wei Wei, Hanli Wang, Yaochu Jin, Kim-Fung Man

Publication date: 11 January 2005

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2004.07.013

zbMATH Keywords

Fuzzy rule base simplificationHierarchical chromosome formulationInterpretabilityMulti-objective decision makingRecursive least square method

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (12)

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules ⋮ Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning ⋮ Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms ⋮ On design of flexible neuro-fuzzy systems for nonlinear modelling ⋮ Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection ⋮ Extraction of fuzzy rules from support vector machines ⋮ A new credibilistic clustering algorithm ⋮ Fuzzy rule base simplification using multidimensional scaling and constrained optimization ⋮ Constructing accurate and parsimonious fuzzy models with distinguishable fuzzy sets based on an entropy measure ⋮ SYSTEMATIC DESIGN PROCEDURE OF TS-TYPE FUZZY CONTROLLERS ⋮ Unnamed Item ⋮ Design of interpretable fuzzy rule-based classifiers using spectral analysis with structure and parameters optimization

Uses Software

• Genocop

Cites Work

• Tackling real-coded genetic algorithms: operators and tools for behavioural analysis
• Genetic algorithms. Concepts and designs
• Extracting interpretable fuzzy rules from RBF networks
• Fuzzy identification of systems and its applications to modeling and control
• Outline of a New Approach to the Analysis of Complex Systems and Decision Processes
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