SVM-RFE peak selection for cancer classification with mass spectrometry data

We studied two cancer classification problems with mass spectrometry data and used SVM-RFE to select a small subset of peaks as input variables for the classification. Our study shows that, SVM-RFE can select a good small subset of peaks with which the classifier achieves high prediction accuracy and the performance is much better than with the feature subset selected by T-statistics. We also found that, the best peak subset selected by SVM-RFE always have in the top ranked peaks by T-statistics while it includes some peaks that are ranked low by T-statistics. However, these peaks together give much better classification performance than the same number of most top ranked peaks by T-statistics. Our experimental comparison of the performance of Support Vector Machine classification algorithm with and without peak selection also consolidates the importance of peak selection for cancer classification with mass spectrometry data. Selecting a small subset of peaks not only improves the efficiency of the classification algorithms, but also improves the cancer classification accuracy, even for classification algorithms like Support Vector Machines, which are capable of handling large number of input variables.