Computers and Industrial Engineering - Special issue: Computational intelligence and information technology applications to industrial engineering selected papers from the 33 rd ICC&IE
International Journal of Approximate Reasoning
Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
Eliciting transparent fuzzy model using differential evolution
Applied Soft Computing
A parallel genetic local search algorithm for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
A neural network-based multi-agent classifier system
Neurocomputing
A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
Computers and Industrial Engineering
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning
IEEE Transactions on Evolutionary Computation
A new method for design and reduction of neuro-fuzzy classification systems
IEEE Transactions on Neural Networks
Unveiling the underlying relationships over a network for monitoring purposes
International Journal of Network Management
Information Sciences: an International Journal
Information Sciences: an International Journal
Analytic network process for pattern classification problems using genetic algorithms
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Information Sciences: an International Journal
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Design and analysis of genetic fuzzy systems for intrusion detection in computer networks
Expert Systems with Applications: An International Journal
Parallel distributed implementation of genetics-based machine learning for fuzzy classifier design
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
A fuzzy rule-based classification system using interval type-2 fuzzy sets
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Expert Systems with Applications: An International Journal
Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis
Journal of Medical Systems
Information Sciences: an International Journal
Ensemble fuzzy rule-based classifier design by parallel distributed fuzzy GBML algorithms
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A hierarchical approach to multi-class fuzzy classifiers
Expert Systems with Applications: An International Journal
GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm
Artificial Intelligence Review
Optimising operational costs using Soft Computing techniques
Integrated Computer-Aided Engineering
Hi-index | 0.01 |
We propose a hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems. First, we examine the search ability of each approach to efficiently find fuzzy rule-based systems with high classification accuracy. It is clearly demonstrated that each approach has its own advantages and disadvantages. Next, we combine these two approaches into a single hybrid algorithm. Our hybrid algorithm is based on the Pittsburgh approach where a set of fuzzy rules is handled as an individual. Genetic operations for generating new fuzzy rules in the Michigan approach are utilized as a kind of heuristic mutation for partially modifying each rule set. Then, we compare our hybrid algorithm with the Michigan and Pittsburgh approaches. Experimental results show that our hybrid algorithm has higher search ability. The necessity of a heuristic specification method of antecedent fuzzy sets is also demonstrated by computational experiments on high-dimensional problems. Finally, we examine the generalization ability of fuzzy rule-based classification systems designed by our hybrid algorithm.