A review of the stability of feature selection techniques for bioinformatics data

Abstract

Feature selection is an important step in data mining and is used in various domains including genetics, medicine, and bioinformatics. Choosing the important features (genes) is essential for the discovery of new knowledge hidden within the genetic code as well as the identification of important biomarkers. Although feature selection methods can help sort through large numbers of genes based on their relevance to the problem at hand, the results generated tend to be unstable and thus cannot be reproduced in other experiments. Relatedly, research interest in the stability of feature ranking methods has grown recently and researchers have produced experimental designs for testing the stability of feature selection, creating new metrics for measuring stability and new techniques designed to improve the stability of the feature selection process. In this paper, we will introduce the role of stability in feature selection with DNA microarray data. We list various ways of improving feature ranking stability, and discuss feature selection techniques, specifically explaining ensemble feature ranking and presenting various ensemble feature ranking aggregation methods. Finally, we discuss experimental procedures such as dataset perturbation, fixed overlap partitioning, and cross validation procedures that help researchers analyze and measure the stability of feature ranking methods. Throughout this work, we investigate current research in the field and discuss possible avenues of continuing such research efforts.