In the present study, we developed the PART-robustBFCS method modified from PART-BFCS. This modeling was performed by using a bagging algorithm. In this algorithm, boosting result was assessed by using the data except one for model construction in order to repress the overfitting by modeling. We applied this method to the analysis of microarray data for the subclass identification of diffuse large B-cell lymphoma (DLBCL) patients. The results of our methods were superior to those of various other methods. The prediction accuracies were 75% for PART-BFCS and 79% for PART-robustBFCS.