# Survey on Feature Selection

arXiv: https://arxiv.org/abs/1510.02892 · HTML: https://mlsanity.com/paper/1510.02892/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2015.

## Built on by (most cited first, 25 of 25)

- [Probabilistic Bilevel Coreset Selection](https://mlsanity.com/paper/2301.09880/) (2023)
- [On Supervised Feature Selection from High Dimensional Feature Spaces](https://mlsanity.com/paper/2203.11924/) (2022)
- [Feature Selection on Quantum Computers](https://mlsanity.com/paper/2203.13261/) (2022)
- [Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition](https://mlsanity.com/paper/2302.13960/) (2023)
- [Where to Pay Attention in Sparse Training for Feature Selection?](https://mlsanity.com/paper/2211.14627/) (2022)
- [Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent](https://mlsanity.com/paper/2403.04015/) (2024)
- [Federated Feature Selection for Cyber-Physical Systems of Systems](https://mlsanity.com/paper/2109.11323/) (2021)
- [ICE-SEARCH: A Language Model-Driven Feature Selection Approach](https://mlsanity.com/paper/2402.18609/) (2024)
- [Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks](https://mlsanity.com/paper/2311.06028/) (2023)
- [Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance](https://mlsanity.com/paper/2411.00851/) (2024)
- [Topological Feature Selection](https://mlsanity.com/paper/2302.09543/) (2023)
- [Distribution Guided Active Feature Acquisition](https://mlsanity.com/paper/2410.03915/) (2024)
- [Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods](https://mlsanity.com/paper/2109.14770/) (2021)
- [Automated Supervised Feature Selection for Differentiated Patterns of Care](https://mlsanity.com/paper/2111.03495/) (2021)
- [A Novel Bio-Inspired Hybrid Multi-Filter Wrapper Gene Selection Method with Ensemble Classifier for Microarray Data](https://mlsanity.com/paper/2101.00819/) (2021)
- [Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel Derivatives](https://mlsanity.com/paper/2404.17344/) (2024)
- [Effect sizes as a statistical feature-selector-based learning to detect breast cancer](https://mlsanity.com/paper/2411.06868/) (2024)
- [BOLIMES: Boruta and LIME optiMized fEature Selection for Gene Expression Classification](https://mlsanity.com/paper/2502.13080/) (2025)
- [Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets](https://mlsanity.com/paper/2105.12005/) (2021)
- [Machine Learning based CVD Virtual Metrology in Mass Produced Semiconductor Process](https://mlsanity.com/paper/2107.05071/) (2021)
- [Accuracy analysis of Educational Data Mining using Feature Selection Algorithm](https://mlsanity.com/paper/2107.10669/) (2021)
- [Sparsity-based Feature Selection for Anomalous Subgroup Discovery](https://mlsanity.com/paper/2201.02008/) (2022)
- [Estimate the building height at a 10-meter resolution based on Sentinel data](https://mlsanity.com/paper/2405.00989/) (2024)
- [UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration](https://mlsanity.com/paper/2411.16693/) (2024)
- [Streamlining HTTP Flooding Attack Detection through Incremental Feature Selection](https://mlsanity.com/paper/2505.17077/) (2025)

## Abstract (arXiv)

Feature selection plays an important role in the data mining process. It is needed to deal with the excessive number of features, which can become a computational burden on the learning algorithms. It is also necessary, even when computational resources are not scarce, since it improves the accuracy of the machine learning tasks, as we will see in the upcoming sections. In this review, we discuss the different feature selection approaches, and the relation between them and the various machine learning algorithms.