A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Active learning with wavelets for microarray data
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Computers and Industrial Engineering
Wavelet selection for disease classification by DNA microarray data
Expert Systems with Applications: An International Journal
Dimensionality reduction and main component extraction of mass spectrometry cancer data
Knowledge-Based Systems
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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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.