  Feature selection is important in data representation and intelligent
diagnosis. Elastic net is one of the most widely used feature selectors.
However, the features selected are dependant on the training data, and their
weights dedicated for regularized regression are irrelevant to their importance
if used for feature ranking, that degrades the model interpretability and
extension. In this study, an intuitive idea is put at the end of multiple times
of data splitting and elastic net based feature selection. It concerns the
frequency of selected features and uses the frequency as an indicator of
feature importance. After features are sorted according to their frequency,
linear support vector machine performs the classification in an incremental
manner. At last, a compact subset of discriminative features is selected by
comparing the prediction performance. Experimental results on breast cancer
data sets (BCDR-F03, WDBC, GSE 10810, and GSE 15852) suggest that the proposed
framework achieves competitive or superior performance to elastic net and with
consistent selection of fewer features. How to further enhance its consistency
on high-dimension small-sample-size data sets should be paid more attention in
our future work. The proposed framework is accessible online
(https://github.com/NicoYuCN/elasticnetFR).
