# Feature Selection: A Data Perspective

arXiv: https://arxiv.org/abs/1601.07996 · HTML: https://mlsanity.com/paper/1601.07996/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2016.

## Built on by (most cited first, 30 of 50)

- [Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection](https://mlsanity.com/paper/2302.13221/) (2023)
- [Data Readiness for AI: A 360-Degree Survey](https://mlsanity.com/paper/2404.05779/) (2024)
- [Towards Task Sampler Learning for Meta-Learning](https://mlsanity.com/paper/2307.08924/) (2023)
- [Fairness-Aware Unsupervised Feature Selection](https://mlsanity.com/paper/2106.02216/) (2021)
- [AFABench: A Generic Framework for Benchmarking Active Feature Acquisition](https://mlsanity.com/paper/2508.14734/) (2025)
- [IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset](https://mlsanity.com/paper/2203.16365/) (2022)
- [Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification](https://mlsanity.com/paper/2106.05841/) (2021)
- [A feature selection method based on Shapley values robust to concept shift in regression](https://mlsanity.com/paper/2304.14774/) (2023)
- [Orthogonal Least Squares Based Fast Feature Selection for Linear Classification](https://mlsanity.com/paper/2101.08539/) (2021)
- [Taming Nonconvexity in Kernel Feature Selection -- Favorable Properties of the Laplace Kernel](https://mlsanity.com/paper/2106.09387/) (2021)
- [Optimizing model-agnostic Random Subspace ensembles](https://mlsanity.com/paper/2109.03099/) (2021)
- [Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithm](https://mlsanity.com/paper/2309.14406/) (2023)
- [Causal Feature Selection via Transfer Entropy](https://mlsanity.com/paper/2310.11059/) (2023)
- [Curvature-based Feature Selection with Application in Classifying Electronic Health Records](https://mlsanity.com/paper/2101.03581/) (2021)
- [Unsupervised Feature Ranking via Attribute Networks](https://mlsanity.com/paper/2111.13273/) (2021)
- [Machine learning for sports betting: should model selection be based on accuracy or calibration?](https://mlsanity.com/paper/2303.06021/) (2023)
- [Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis](https://mlsanity.com/paper/2303.14734/) (2023)
- [Feature Selection: A perspective on inter-attribute cooperation](https://mlsanity.com/paper/2306.16559/) (2023)
- [Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette](https://mlsanity.com/paper/2309.02272/) (2023)
- [Group-Feature (Sensor) Selection With Controlled Redundancy Using Neural Networks](https://mlsanity.com/paper/2310.20524/) (2023)
- [Iterative missing value imputation based on feature importance](https://mlsanity.com/paper/2311.08005/) (2023)
- [Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning](https://mlsanity.com/paper/2401.06251/) (2024)
- [LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method](https://mlsanity.com/paper/2401.12683/) (2024)
- [Granular conditional entropy-based attribute reduction for partially labeled data with proxy labels](https://mlsanity.com/paper/2101.09495/) (2021)
- [Scalable variable selection for two-view learning tasks with projection operators](https://mlsanity.com/paper/2307.01558/) (2023)
- [Finding Optimal Diverse Feature Sets with Alternative Feature Selection](https://mlsanity.com/paper/2307.11607/) (2023)
- [Predicting Properties of Nodes via Community-Aware Features](https://mlsanity.com/paper/2311.04730/) (2023)
- [Introducing 3DCNN ResNets for ASD full-body kinematic assessment: a comparison with hand-crafted features](https://mlsanity.com/paper/2311.14533/) (2023)
- [A novel feature selection framework for incomplete data](https://mlsanity.com/paper/2312.04171/) (2023)
- [Using Constraints to Discover Sparse and Alternative Subgroup Descriptions](https://mlsanity.com/paper/2406.01411/) (2024)

## Abstract (arXiv)

Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data mining and machine learning problems. The objectives of feature selection include: building simpler and more comprehensible models, improving data mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities to feature selection. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. Motivated by current challenges and opportunities in the era of big data, we revisit feature selection research from a data perspective and review representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for conventional data, we categorize them into four main groups: similarity based, information theoretical based, sparse learning based and statistical based methods. To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (urlhttp://featureselection.asu.edu/). Also, we use it as an example to show how to evaluate feature selection algorithms. At the end of the survey, we present a discussion about some open problems and challenges that require more attention in future research.