Abstract:

Feature selection algorithms are an important tool for microarray data analysis, thus their classification ability and stability are essential for data analysis. We propose an ensemble feature selection algorithm for high dimensional microarray data to compensate for the lack of information on a single gene subset. We firstly adopt the signal noise ratio method to select discriminative genes, and then generate relevant gene subsets by evaluating the correlation between the candidate gene and discriminative gene through conditional correlation coefficients. We finally integrate resemblant gene subsets through the ensemble learning technology. Experimental results show that in most cases the classification ability and stability of the proposed algorithm is superior to those that select only a single gene subset.

Key words: microarray data;signal noise ratio;conditional correlation coefficient;feature selection