Stability of feature selection algorithms and its influence on prediction accuracy in biomedical datasets

Feature selection techniques become significant part of many bioinformatics and biomedical applications. Choosing the important features is essential for biomarker discovery, provide better understanding of the data and potentially improve prediction performance. However, as the number of samples in dataset is small, the feature selection tends to be unstable. In this paper, the stability of five popular feature selection techniques is compared and analyzed when original dataset is perturbed by adding, removing or simply resampling the original observations. Next, the feature selection techniques are used as filter prior to AdaBoost classifier and their influence on classification accuracy and Mathews correlation coefficient is compared.