DNA Microarray Data Analysis Based on Optimal Orthogonal Centroid Feature Selection

With the development of DNA microarray technology,thousands of gene expressions can be observed simultaneously.Microarray data has the feature of high dimensions and small samples,which brings difficulty to the analysis.It is important and meaningful to select or discover informative genes from microarray data.This paper employs an optimal orthogonal centroid feature selection algorithm(OCFS) to select the informative genes and compares it with gene selection method based on signal noise ratio and gene selection method based on genetic algorithm.Finally,the support vector machine(SVM) is used to classify the data set.This method is applied to a classic microarray data set(leukemia data) and(achieved) 33/34 classification accuracy on the test data set with 34 samples.