C4.5: programs for machine learning
C4.5: programs for machine learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Fuzzy Modeling for Control
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Genetic Algorithms for Feature Selection and Weighting, A Review and Study
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Simulated Annealing Using a Reversible Jump Markov Chain Monte Carlo Algorithm for Fuzzy Clustering
IEEE Transactions on Knowledge and Data Engineering
A feature selection technique for classificatory analysis
Pattern Recognition Letters
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
WSEAS Transactions on Systems and Control
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In this paper we present an hybrid approach which integrate Fuzzy C-Means (FCM) algorithms and Genetic Algorithms (GAs) to design an optimal classifier for the specific classification problem. This integration allows automatic generation of an classifier system, with an optimized subset of features, from a database of examples. The generated classifier strongly outperform the classic FCM algorithm. A reasoned implementation of the hybrid algorithm, we called GFCM, is given along with a comparative study and performance evaluation results on several public benchmark databases. Results obtained show the efficiency of GFCM algorithm.