Feature Selection

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Overview

Feature selection studies which variables, channels, bands, or learned dimensions carry useful information, aiming to simplify models and improve prediction, interpretation, or measurement efficiency. Common approaches include Lasso and elastic net regularization, filters such as mRMR and Boruta, Bayesian spike-and-slab models, and wrapper or evolutionary searches; newer methods use neural gates, attribution scores, and sequential policies. Unsupervised methods select features for clustering or representation learning, while applications span tabular data, omics, EEG, hyperspectral imagery, and time series. A central challenge is that selected features can change with correlated inputs, evaluation design, or distribution shifts, so predictive accuracy must be considered alongside stability, error control, computational cost, and strong baselines.

Latest papers

CardsList

196 papers27 papers in the last four weeks, up 59% on the four weeks before. 0.2% of all new papers.

  1. MinShap: A Modified Shapley Value Approach for Feature Selection

    Apr 16, 2026Chenghui Zheng, Garvesh RaskuttiShapley Value AttributionFeature Selection

    Department of Statistics, University of Wisconsin - Madison, Madison, WI 53706

  2. Unsupervised feature selection using Bayesian Tucker decomposition

    Apr 16, 2026Y-h. Taguchi, Yoh-ichi MototakeTensor DecompositionFeature Selection

    Department of Physics, Chuo University, Tokyo, 112-8551, Japan. · Graduate School of Social Data Science, Hitotsubashi University, Tokyo, 186-8601, Japan.

  3. Mamba-SSM with LLM Reasoning for Feature Selection: Faithfulness-Aware Biomarker Discovery

    Apr 15, 2026Pushpa Kumar Balan, Aijing FengFeature SelectionScientific Discovery

    Department of Computer Science and Cybersecurity, University of Central Missouri

  4. SMART Fine-tuning Factor Augmented Neural Lasso

    Apr 14, 2026Jinhang Chai, Jianqing Fan, Cheng Gao +1Fine-TuningTransfer Learning

    Department of Operations Research and Financial Engineering, Princeton University

  5. NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity

    Apr 13, 2026Zhaoyu Xing, Xiufan YuHeterogeneous GNNsFeature Selection

    Department of Applied and Computational Mathematics and Statistics, University of Notre Dame

  6. DINO-VO: Learning Where to Focus for Enhanced State Estimation

    Apr 5, 2026Qi Chen, Guanghao Li, Sijia Hu +4Visual OdometryFeature Selection

    Fudan University · Shanghai Innovation Institute

  7. Biconvex Biclustering

    Apr 5, 2026Sam Rosen, Eric C. Chi, Jason XuClusteringFeature Selection

    Department of Statistical Science, Duke University · School of Statistics, University of Minnesota · Department of Biostatistics, University of California Los Angeles

  8. Sparse Regression under Correlation and Weak Signals: A Reproducible Benchmark of Classical and Bayesian Methods

    Apr 4, 2026Hao XiaoSparse RecoveryLinear Regression

    ATLAS AI Lab

  9. Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks

    Apr 4, 2026Benjamin S. Knight, Ahsaas BajajRidge RegressionModel Selection

  10. Dynamic Graph Neural Network with Adaptive Features Selection for RGB-D Based Indoor Scene Recognition

    Apr 1, 2026Qiong Liu, Ruofei Xiong, Xingzhen Chen +2Multimodal Graph LearningDynamic Graph Learning

    School of Electronic Information and Communications, Huazhong University of Science and Technology, No. 1037, Luoyu Rd., Wuhan, 430074, P. R. China

  11. Genetic algorithms for multi-omic feature selection: a comparative study in cancer survival analysis

    Mar 31, 2026Luca Cattelani, Vittorio FortinoFeature SelectionGenetic Algorithms

    Institute of Biomedicine, School of Medicine, University of Eastern Finland, Yliopistonranta 8, 70210, Finland

  12. KGroups: A Versatile Univariate Max-Relevance Min-Redundancy Feature Selection Algorithm for High-dimensional Biological Data

    Mar 30, 2026Malick Ebiele, Malika Bendechache, Rob BrennanBioinformaticsFeature Selection

    Computer Science, University College Dublin, Dublin, Dublin, Ireland. · ADAPT Centre, Dublin, Dublin, Ireland. · Computer Science, University of Galway, Galway, Galway, Ireland.

  13. A Compression Perspective on Simplicity Bias

    Mar 26, 2026Tom Marty, Eric Elmoznino, Leo Gagnon +5Neural Network GeneralizationFeature Selection

    Mila – Quebec AI Institute · Université de Montréal

  14. LLM-Driven Reasoning for Constraint-Aware Feature Selection in Industrial Systems

    Mar 26, 2026Yuhang Zhou, Zhuokai Zhao, Ke Li +14Feature Selection

    Meta AI

  15. Minimal Sufficient Representations for Self-interpretable Deep Neural Networks

    Mar 25, 2026Zhiyao Tan, Liu Li, Huazhen LinNeural Network InterpretabilityRepresentation Learning

    Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, China · School of Mathematics and Statistics, Wuhan University, Wuhan, China · New Cornerstone Science Laboratory, Southwestern University of Finance and Economics, Chengdu, China

  16. i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data

    Mar 25, 2026Chen Ma, Wanjie Wang, Shuhao FanUnsupervised ClusteringFeature Selection

    SUSTech · NUS

  17. MOELIGA: a multi-objective evolutionary approach for feature selection with local improvement

    Mar 21, 2026Leandro Vignolo, Matias GerardFeature SelectionGenetic Algorithms

    Research Institute for Signals, Systems and Computational Intelligence (sınc(i)), FICH–UNL/CONICET, Ciudad Universitaria UNL, Santa Fe, Argentina.

  18. Causally-Guided Diffusion for Stable Feature Selection

    Mar 21, 2026Arun Vignesh Malarkkan, Xinyuan Wang, Kunpeng Liu +2Feature SelectionDistribution Shift Robustness

    Arizona State University Tempe, Arizona, USA · Clemson University Clemson, South Carolina, USA · Stevens Institute of Technology Hoboken, New Jersey, USA

  19. BVSIMC: Bayesian Variable Selection-Guided Inductive Matrix Completion for Improved and Interpretable Drug Discovery

    Mar 19, 2026Sijian Fan, Liyan Xiong, Dayuan Wang +2Robust Matrix CompletionDrug Discovery

    Department of Statistics, George Mason University

  20. Safe Distributionally Robust Feature Selection under Covariate Shift

    Mar 17, 2026Hiroyuki Hanada, Satoshi Akahane, Noriaki Hashimoto +2Feature SelectionDistributionally Robust Optimization

    Graduate School of Engineering, Nagoya University, Nagoya, Japan · Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan

  21. Sampling-guided exploration of active feature selection policies

    Mar 16, 2026Gabriel Bernardino, Anders Jonsson, Patrick Clarysse +1Active Feature AcquisitionFeature Selection

    Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain · Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69621, Lyon, France · Institut Universitaire de France (IUF)

  22. Is the reconstruction loss culprit? An attempt to outperform JEPA

    Mar 14, 2026Alexey Potapov, Oleg Shcherbakov, Ivan KravchenkoPredictive Representation LearningAutoencoders

    SingularityNET

  23. An Interpretable and Stable Framework for Sparse Principal Component Analysis

    Mar 14, 2026Ying Hu, Hu YangPrincipal Component AnalysisDimensionality Reduction

    College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China

  24. A Grid-Based Framework for E-Scooter Demand Representation and Temporal Input Design for Deep Learning: Evidence from Austin, Texas

    Mar 13, 2026Mohammad Sahnoon, Merkebe Getachew Demissie, Roberto SouzaSpatiotemporal ForecastingFeature Selection

    Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada · Department of Civil Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada

  25. Relaxed Efficient Acquisition of Context and Temporal Features

    Mar 11, 2026Yunni Qu, Dzung Dinh, Grant King +5Active Feature AcquisitionFeature Selection

    The University of North Carolina at Chapel Hill · University of North Carolina at Chapel Hill, Chapel Hill, NC, USA · University of Michigan +3

  26. A New Modeling to Feature Selection Based on the Fuzzy Rough Set Theory in Normal and Optimistic States on Hybrid Information Systems

    Mar 9, 2026Mohammad Hossein Safarpour, Seyed Majid Alavi, Mohammad Izadikhah +1Feature Selection

    Department of Mathematics and Computer Science, Arak Branch, Islamic Azad University, Arak, Iran

  27. What on Earth is AlphaEarth? Hierarchical structure and functional interpretability for global land cover

    Mar 8, 2026Ivan Felipe Benavides-Martinez, Justin Guthrie, Jhon Edwin Arias +6Geospatial Foundation ModelsGeospatial Representation Learning

    Artificial Intelligence for Climate and Sustainability The Institute for Experiential Artificial Intelligence Northeastern University, Portland, ME, USA · Gulf of Maine Research Institute Portland, ME, USA · Sustainability and Data Sciences Laboratory Northeastern University, Boston, MA, USA +2

  28. SPPCSO: Adaptive Penalized Estimation Method for High-Dimensional Correlated Data

    Mar 6, 2026Ying Hu, Hu YangFeature Selection

    College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China.

  29. SEHFS: Structural Entropy-Guided High-Order Correlation Learning for Multi-View Multi-Label Feature Selection

    Mar 3, 2026Cheng Peng, Yonghao Li, Wanfu Gao +2Feature SelectionMulti-View Learning

    the College of Software, Jilin University, Changchun 130012, China · the Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China · the School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China +5

  30. Beyond False Discovery Rate: A Stepdown Group SLOPE Approach for Grouped Variable Selection

    Mar 1, 2026Xuelin Zhang, Jingxuan Liang, Xinyue Liu +2Feature SelectionFDR Control

    College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Hubei Province, China.