A Robust Method for Generating Discriminative Gene Clusters

Microarray technology is often used to identify the genes that are differentially expressed between two biological conditions. Since microarray datasets contain a small number of samples and a large number of genes, it is not difficult to find small gene subsets which are highly discriminative. However, such identified classifiers tend to have poor generalization properties on the test samples due to overfitting. We propose a novel approach for generating discriminative gene clusters. Our experiments on both simulated and real datasets show that our method can generate a series of robust gene clusters with good classification performance.