A significant problem in gene expression analysis is that the sample size is substantially lower than the number of genes. Bagging is an effective method of solving this problem in the case of small sample datasets. We have devised a combination method, called the BagPART filtering method, that uses the projective adaptive resonance theory (PART) to select important genes and achieve a binary classification more accurately (p<10(-10)) than conventional methods, particularly when the sample size is small.