The bitmap-based feature selection method
Proceedings of the 2003 ACM symposium on Applied computing
A novel feature selection method for large-scale data sets
Intelligent Data Analysis
An efficient bit-based feature selection method
Expert Systems with Applications: An International Journal
A multilayered neuro-fuzzy classifier with self-organizing properties
Fuzzy Sets and Systems
International Journal of Systems Science
A hybrid GA-based fuzzy classifying approach to urinary analysis modeling
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
FR3: a fuzzy rule learner for inducing reliable classifiers
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Tracking control of uncertain DC server motors using genetic fuzzy system
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Expert Systems with Applications: An International Journal
An interpretable classification rule mining algorithm
Information Sciences: an International Journal
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Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes. Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules. SLAVE can select the relevant features of the domain, but when working with large databases the search space is too large and the running time can sometimes be excessive. We propose to improve SLAVE by including a feature selection model in which the genetic algorithm works with individuals (representing individual rules) composed of two structures: one structure representing the relevance status of the involved variables in the rule, the other one representing the assignments variable/value. For this general representation, we study two alternatives depending on the information coded in the first structure. When compared with the initial algorithm, this new approach of SLAVE reduces the number of rules, simplifies the structure of the rules and improves the total accuracy