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

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

  1. LUMOS: Democratizing SciML Workflows with L0-Regularized Learning for Unified Feature and Parameter Adaptation

    Feb 26, 2026Shouwei Gao, Xu Zheng, Dongsheng Luo +2Scientific MLAutomated Machine Learning

    Oregon State University · Florida International University · Argonne National Laboratory

  2. X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation

    Feb 25, 2026Abdul Karim Gizzini, Yahia MedjahdiChannel EstimationEfficient Neural Network Inference

    University of Paris-Est Créteil (UPEC), LISSI/TincNET, F-94400, Vitry-sur-Seine, France. · IMT Nord Europe, Institut Mines Télécom, Centre for Digital Systems, F-59653 Villeneuve d’Ascq, France.

  3. Sparse Bayesian Deep Functional Learning with Structured Region Selection

    Feb 24, 2026Xiaoxian Zhu, Yingmeng Li, Shuangge Ma +1Functional Data AnalysisBayesian Neural Networks

    School of Statistics and Data Science, Shanghai University of Finance and Economics · Department of Biostatistics, Yale School of Public Health

  4. FairFS: Addressing Deep Feature Selection Biases for Recommender System

    Feb 23, 2026Xianquan Wang, Zhaocheng Du, Jieming Zhu +3Feature SelectionRecommender Systems

    Huawei Noah’s Ark Lab, Beijing, China

  5. Valid Feature-Level Inference for Tabular Foundation Models via the Conditional Randomization Test

    Feb 19, 2026Mohamed SalemStatistical Hypothesis TestingFeature Selection

  6. ML-driven detection and reduction of ballast information in multi-modal datasets

    Feb 18, 2026Yaroslav SolovkoFeature SelectionDimensionality Reduction

    Independent Researcher, Dublin, Ireland

  7. Separating Oblivious and Adaptive Models of Variable Selection

    Feb 18, 2026Ziyun Chen, Jerry Li, Kevin Tian +1Sparse RecoveryFeature Selection

    University of Washington · University of Texas at Austin

  8. ROOFS: RObust biOmarker Feature Selection

    Jan 8, 2026Anastasiia Bakhmach, Paul Dufossé, Simon Charpigny +4Scientific DiscoveryClinical Prediction

    1*COMPO Team (COMPutational Pharmacology and clinical Oncology), Inria Sophia Antipolis – Méditerranée, Inserm U1068, CNRS UMR7258, Institut Paoli-Calmettes, Aix-Marseille University, Marseille, France. · 2Veracyte SAS, Marseille, France. · 3Assistance Publique-Hôpitaux de Marseille (APHM), Marseille, France. +1

  9. Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond

    Dec 4, 2025Kazuma SawayaFDR ControlFeature Selection

    The University of Tokyo, Bunkyo, Tokyo, Japan

  10. Survival of the fittest Cox model: Pivotal variable selection for time-to-event data

    Oct 22, 2025Maxime van Cutsem, Sylvain SardyModel SelectionSurvival Analysis

    Department of Mathematics, University of Geneva

  11. A Compositional Kernel Model for Feature Learning

    Sep 17, 2025Feng Ruan, Keli Liu, Michael JordanKernel Ridge RegressionKernel Regression

    *Department of Statistics and Data Science, Northwestern University. · *Company E · *Department of Statistics and Computer Science, University of California, Berkeley.

  12. Counterfactual Operator Relevance for PDE Discovery: Screening, Pruning, and Identifiability

    Jun 25, 2025Ronald KatendeCounterfactual EvaluationPDE Discovery

    Department of Mathematics Kabale University Kikungiri Hill, Katuna Road, 317, Kabale, Uganda

  13. Scalable unsupervised feature selection via weight stability

    Jun 6, 2025Xudong Zhang, Renato Cordeiro de AmorimUnsupervised LearningClustering

    School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe, UK.

  14. Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection

    Apr 1, 2025Ruoyu Chen, Siyuan Liang, Jingzhi Li +4Feature AttributionVLM Interpretability

    Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China · University of Chinese Academy of Sciences, Beijing 100049, China · College of Computing and Data Science, Nanyang Technological University, 639798, Singapore +4

  15. Feature Selection for Latent Factor Models

    Dec 13, 2024Rittwika Kansabanik, Adrian BarbuFeature Selection

    Department of Statistics, Florida State University

  16. Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts

    Jul 14, 2019Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van +1Mixture of ExpertsSparse Mixture-of-Experts

    Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam · IRT SystemX, 91120 Palaiseau, France +1