COR: a methodology to improve ad hoc data-driven linguistic rulelearning methods by inducing cooperation among rules

Authors:
J. Casillas;O. Cordon;F. Herrera
Affiliations:
Dept. of Comput. Sci. & Artificial Intelligence, Granada Univ.;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2002

Citing 0
Cited 20

Improving Simple Linguistic Fuzzy Models by Means of the Weighted COR Methodology

IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
A Prediction System for Cardiovascularity Diseases Using Genetic Fuzzy Rule-Based Systems

IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Initial breeding value prediction on Manchego sheep by using rule-based systems

Expert Systems with Applications: An International Journal
Learning weighted linguistic fuzzy rules by using specifically-tailored hybrid estimation of distribution algorithms

International Journal of Approximate Reasoning
Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework

International Journal of Approximate Reasoning
An Attempt to Use the KEEL Tool to Evaluate Fuzzy Models for Real Estate Appraisal

Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
Concept of a Multi-Agent System for Assisting in Real Estate Appraisals

KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
An interpretable fuzzy rule-based classification methodology for medical diagnosis

Artificial Intelligence in Medicine
A fuzzy modeling method via Enhanced Objective Cluster Analysis for designing TSK model

Expert Systems with Applications: An International Journal
A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems

Expert Systems with Applications: An International Journal
Comparative Analysis of Evolutionary Fuzzy Models for Premises Valuation Using KEEL

ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Genetic tuning for improving Wang and Mendel's fuzzy database

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Comparison of data driven models for the valuation of residential premises using KEEL

International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Comparing two genetic overproduce-and-choose strategies for fuzzy rule-based multiclassification systems generated by bagging and mutual information-based feature selection

International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Exploration of bagging ensembles comprising genetic fuzzy models to assist with real estate appraisals

IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems

International Journal of Approximate Reasoning
A disaster-severity assessment DSS comparative analysis

OR Spectrum
Fuzzy transforms method in prediction data analysis

Fuzzy Sets and Systems
Learning cooperative TSK-0 fuzzy rules using fast local search algorithms

CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a new learning methodology to quickly generate accurate and simple linguistic fuzzy models: the cooperative rules (COR) methodology. It acts on the consequents of the fuzzy rules to find those that are best cooperating. Instead of selecting the consequent with the highest performance in each fuzzy input subspace, as ad-hoc data-driven methods usually do, the COR methodology considers the possibility of using another consequent, different from the best one, when it allows the fuzzy model to be more accurate thanks to having a rule set with the best cooperation. Our proposal has shown good results in solving three different applications when compared to other methods