Stability of feature selection algorithm: A review

Feature selection technique is a knowledge discovery tool which provides an understanding of the problem through the analysis of the most relevant features. Feature selection aims at building better classifier by listing significant features which also helps in reducing computational overload. Due to existing high throughput technologies and their recent advancements are resulting in high dimensional data due to which feature selection is being treated as handy and mandatory in such datasets. This actually questions the interpretability and stability of traditional feature selection algorithms. The high correlation in features frequently produces multiple equally optimal signatures, which makes traditional feature selection method unstable and thus leading to instability which reduces the confidence of selected features. Stability is the robustness of the feature preferences it produces to perturbation of training samples. Stability indicates the reproducibility power of the feature selection method. High stability of the feature selection algorithm is equally important as the high classification accuracy when evaluating feature selection performance. In this paper, we provide an overview of feature selection techniques and instability of the feature selection algorithm. We also present some of the solutions which can handle the different source of instability.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an

Automated summary

Feature selection technique is a tool for understanding problems by analyzing relevant features, which can improve classifier performance and reduce computational load. However, the high correlation between features often leads to instability in traditional feature selection algorithms, resulting in reduced confidence in the selected features. Therefore, achieving high stability in feature selection algorithms is crucial.