  The current study proposes a dimension reduction method, stepwise support
vector machine (SVM), to reduce the dimensions of large p small n datasets. The
proposed method is compared with other dimension reduction methods, namely, the
Pearson product difference correlation coefficient (PCCs), recursive feature
elimination based on random forest (RF-RFE), and principal component analysis
(PCA), by using five gene expression datasets. Additionally, the prediction
performance of the variables selected by our method is evaluated. The study
found that stepwise SVM can effectively select the important variables and
achieve good prediction performance. Moreover, the predictions of stepwise SVM
for reduced datasets was better than those for the unreduced datasets. The
performance of stepwise SVM was more stable than that of PCA and RF-RFE, but
the performance difference with respect to PCCs was minimal. It is necessary to
reduce the dimensions of large p small n datasets. We believe that stepwise SVM
can effectively eliminate noise in data and improve the prediction accuracy in
any large p small n dataset.
