Robust feature selection by weighted fisher criterion for multiclass prediction in gene expression profiling

This paper presents a robust feature selection approach for multiclass prediction with application to microarray studies. First, individually discriminatory genes (IDGs) are identified by using weighted Fisher Criterion (wFC). Second, jointly discriminatory genes (JDGs) are selected by a sequential search method, according to their joint class separability. To combat the small size effect on feature selection, leave-one-out procedures are incorporated into both IDG and JDG selection steps to improve the robustness of the approach. By applying this approach to a microarray study of small round blue cell tumors (SRBCTs) of childhood, we have demonstrated that our robust feature selection method can be used to successfully identify a subset of genes with superior classification performance for multiclass prediction.