A hybrid GA & back propagation approach for gene selection and classification of microarray data

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.

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