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This document discusses using wavelet analysis and genetic algorithms to detect key genes in microarray data classification. It first uses wavelet transforms to extract approximation coefficients at the second level to reduce noise and dimensionality. Then it uses genetic algorithms to select optimized feature genes from the approximation coefficients. Experiments on four datasets show the approximation coefficients characterize the microarray data well. It then reconstructs the approximation part to identi

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