Stability of feature selection algorithm: A review

By U. Khaire, R. Dhanalakshmi. Journal of King Saud University: Computer and Information Sciences, 2019

610 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3bccf31bb0cada2e7b24fab7d60abf921f940a6d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Molecular Biology

Citations in the corpus

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction (2020, methodology): “It uses statistical standards for evaluating the ranking of the subset [17, 24].”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, methodology): “variable ranking) and Subset Evaluation Based [61].”
Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, background): “The possible solution to make the algorithm stable for feature selection problem can be found in [252], [253].”