Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM
This addresses the problem of noise and prediction accuracy in large p small n datasets, such as gene expression data, but is incremental as it builds on existing SVM and dimension reduction techniques.
The study tackled dimension reduction for high-dimensional datasets with many variables but few samples by proposing a stepwise SVM method, finding it effectively selects important variables and achieves better prediction performance than unreduced datasets, with stable results compared to PCA and RF-RFE.
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.