RISC: A new filter approach for feature selection from proteomic data

This paper proposes a novel feature selection technique for SELDI-TOF spectrum data. The new technique, called RISC (Relevance Index by Sample Counting), measures the relevance of features based on each sample's discriminating power to partition the samples in the opposite class. We also proposes a heuristic searching method to obtain the optimal feature set, which makes use of the relevance parameters. Our technique is fast even for extremely high-dimensional datasets such as SELDI spectrum, since it has low computational complexity and consists of simple counting operations. The new technique also shows good performance comparable to the conventional feature selection techniques from the experiment on three clinical datasets from NCI/CCR and FDA/CBER Clinical Proteomics Program Databank: Ovarian 4-3-02, Ovarian 7-8-02, Prostate.