Wavelet feature selection for microarray data

A hybrid method of feature selection based on wavelet analysis and genetic algorithm (GA) is proposed in this study for high dimensional microarray data. A set of orthogonal wavelet approximation coefficients based on wavelet decomposition are extracted to compress the gene profiles and reduce the dimensionality of microarray data. Then genetic algorithm is performed to select the optimized features from approximation coefficients. Linear discriminant analysis (LDA) is employed to evaluate the classification performance. Experiments are performed on four datasets. Our results show that this hybrid method is efficient and robust

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