Comparison of stability measures for feature selection

Abstract

The feature selection is inevitable part of machine learning techniques in biomedical engineering and bioinformatics. Feature selection methods are used to select the most discriminative features, e.g. for disease classification. Even if there are plenty of feature selection methods the stability of these algorithms is still open question. Another issue with assessing the stability of feature selection is that there are several stability measures providing different views on stability. Here, we compare well-known stability measures and evaluate their performance on artificial and real data.