Wavelet feature extraction for high-dimensional microarray data

It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition. Original microarray data are usually of high dimensionality, whereas, only limited training samples are available. Therefore, dimensionality reduction is an important strategy to greatly improve the classification performance of microarray data. A novel method of feature extraction and dimensionality reduction for high-dimensional microarray data is proposed in this study. A set of orthogonal wavelet detail coefficients based on wavelet decomposition at different levels is extracted to characterize the localized features of microarray data. Experiments are carried out on four datasets. A highly competitive accuracy is achieved in comparison with the performance of other models.