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

This work presents a robust feature selection approach for multiclass prediction with application to microarray studies. First, individually discriminatory genes (IDGs) are identified using a 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.