An Improved SVM-RFE Based on $F$ -Statistic and mPDC for Gene Selection in Cancer Classification

As two major parts for tackling high-dimensional cancer microarray gene data sets, feature selection and classification have attracted an increasing interest in academia and medical community. Since cancer gene expression data sets have small samples, high dimensionality, and class imbalance problems, extracting useful gene information and effective classification becomes more challenging. In this paper, we propose a novel feature selection algorithm called ISVM-RFE(FPD) for classification, which fully utilizes classification performance of each feature subset. Compared to the existing algorithms, ISVM-RFE(FPD) takes into account not only the intrinsic characteristic of the data, but also both linear and nonlinear correlation among features. The experimental results demonstrate that ISVM-RFE(FPD) outperforms the existing SVM-based feature selection algorithms in terms of recall rate of positive samples (rr
_p
) and G-mean (G).