The nature of statistical learning theory
The nature of statistical learning theory
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
A novel feature selection method to improve classification of gene expression data
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Enhancing genetic feature selection through restricted search and Walsh analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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We propose a Genetic Algorithm (GA) approach combined with Neural Network (MultiLayer Perceptron) with Back Propagation algorithm (BP) for the classification of high dimensional Microarray data. This approach is associated to a fuzzy logic based pre-filtering technique. The GA is used to evolve gene subsets whose fitness is evaluated by a NN classifier. Using archive records of "good" gene subsets, a frequency based technique is introduced to identify the most informative genes. Our approach is assessed on two well-known cancer datasets and shows competitive results with six existing methods.