Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms

DOI10.1007/s00500-008-0362-4zbMath1181.68215OpenAlexW1979652651MaRDI QIDQ1006921

J. Martínez

Publication date: 26 March 2009

Published in: Soft Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00500-008-0362-4

zbMATH Keywords

multiobjective genetic cooperative competitive learning algorithm

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

A framework for learning fuzzy rule-based models with epistemic set-valued data and generalized loss functions ⋮ Fuzzy transforms method in prediction data analysis ⋮ Inner and outer fuzzy approximations of confidence intervals ⋮ Machine learning models, epistemic set-valued data and generalized loss functions: an encompassing approach ⋮ Statistical reasoning with set-valued information: ontic vs. epistemic views

Uses Software

• SPEA2
• KEEL

Cites Work

• Unnamed Item
• Higher order models for fuzzy random variables
• Genetic learning of fuzzy rules based on low quality data
• The mean value of a fuzzy number
• Multivariate adaptive regression splines
• Some relationships between fuzzy and random set-based classifiers and models
• A fast genetic method for inducting descriptive fuzzy models.
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms
• Fuzzy identification of systems and its applications to modeling and control
• Matching pursuits with time-frequency dictionaries
• Boosting fuzzy rules in classification problems under single-winner inference
• Evolutionary Multi-Criterion Optimization

This page was built for publication: Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms