Spectral Pattern Comparison Methods for Cancer Classification Based on Microarray Gene Expression Data

We present, in this paper, two spectral pattern comparison methods for cancer classification using microarray gene expression data. The proposed methods are different from other current classifiers in the ways features are selected and pattern similarities measured. In addition, these spectral methods do not require any data preprocessing which is necessary for many other classification techniques. Experimental results using three popular microarray data sets demonstrate the robustness and effectiveness of the spectral pattern classifiers

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