A Novel Metric for Redundant Gene Elimination Based on Discriminative Contribution

As a high dimensional problem, analysis of microarray data sets is a hard task, where many weakly relevant but redundant features hurt generalization performance of classifiers. There are previous works to handle this problem by using linear or nonlinear filters, but these filters do not consider discriminative contribution of each feature by utilizing the label information. Here we propose a novel metric based on discriminative contribution to perform redundant feature elimination. By the new metric, complementary features are likely to be reserved, which is beneficial for the final classification. Experimental results on several microarray data sets show our proposed metric for redundant feature elimination based on discriminative contribution is better than the previous state-of-arts linear or nonlinear metrics on the problem of analysis of microarray data sets.