Feature Selection

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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

194 papers26 papers in the last four weeks, up 53% on the four weeks before. 0.2% of all new papers.

Post-Hoc Feature Selection Layer for Neural Networks Interpretability
Oct 22, 2026João Kenji Suwa, João Pedro Silveira e Silva, Bruno Iochins GrisciNeural Network Interpretability Feature Attribution
Institute of Informatics, UFRGS, Porto Alegre, Brazil
Truncated Out-of-Bag Probability Gain: A Noise-Robust Criterion for Random-Forest Wrapper Feature Selection
Oct 21, 2026Sejong OhFeature Selection Random Forest
Department of Software Science, Dankook University, Youngin, Republic of Korea
Policy-Mask: Budget-Aware Adaptation of Diffusion Features to Discriminative Tasks
Oct 20, 2026Ivan Listopadov, Maksim Nakhodnov, Aibek AlanovDiffusion Model Adaptation Feature Selection
HSE University · MSU · Constructor University +1
When Do LLMs Help with Feature Selection? A Systematic Empirical Study
Oct 20, 2026Fateme Memar, Dongjie Wang, Arvin AgahFeature Selection Tabular ML
Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA · School of Information Science and Technology, Northeast Normal University, China
Multi-Objective Evolutionary Semi-Supervised Learning for Resource-Aware Smart City Classification
Oct 20, 2026Francisco José Sedeño, Jamal ToutouhSemi-Supervised Learning Tabular Classification
ITIS Software, University of Malaga, Malaga, Spain
How Robust Are Prognostic Radiomic Features? Comparing Selection Techniques on Pooled Glioblastoma Cohorts
Oct 19, 2026Yannick Sodonougbo, Marlene Schnieders, Alexia Rizoudis +3Radiomics Feature Selection
IT Infrastructure for Translational Medical Research, University of Augsburg · Department of Neurosurgery, Río Hortega University Hospital · Specialized Group in Biomedical Imaging and Computational Analysis, Instituto de Investigación Biosanitaria de Valladolid +1
A Feature Selection Layer Approach for Multi-Class Classification on Neural Networks
Oct 16, 2026João Pedro Silveira e Silva, Márcio Dorn, Bruno Iochins GrisciFeature Selection Feature Attribution
Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil · Center for Biotechnology, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil · National Institute of Science and Technology - Forensic Science, Porto Alegre, RS, Brazil
SelectPFN: Amortized Bayesian Variable Selection for Sparse Nonlinear Regression with Prior-Data Fitted Networks
Oct 15, 2026Bin LuoFeature Selection Prior-Data Fitted Networks
School of Data Science and Analytics, Kennesaw State University
Learning to Select Features with Manifold Deep Unfolding PCA
Oct 14, 2026Jingjing Liu, Pei Tai, Xianchao Xiu +1Feature Selection Principal Component Analysis
Shanghai Key Laboratory of Automobile Intelligent Network Interaction Chip and System, School of Microelectronics, Shanghai University, Shanghai 200444, China · School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China · School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations
Oct 12, 2026Nicola Rossberg, Bennett Kleinberg, Barry O'Sullivan +2Explainable Artificial Intelligence Feature Selection
School of Computer Science and Information Technology, University College Cork, Ireland · Centre for Research Training in Artificial Intelligence, University College Cork, Ireland · Department of Methodology and Statistics, Tilburg University +1
Screening Collapse under Recursive Synthetic Data
Oct 11, 2026Jia Zhang, Xin ChenFeature Selection Statistical Learning Theory
Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China · Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China
Random Feature Pruning for Multi-Class Classification
Oct 10, 2026Mohammad Amin Omidi, Babak Seyfe, Shahrokh ValaeeFeature Selection Random Feature Methods
Information Theoretic Learning Systems Laboratory (ITLSL), Department of Electrical Engineering, Shahed University, Tehran, Iran · Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems
Oct 9, 2026Vishnu Arun, Adam Obeng, Tianyou Wang +7Recommender Systems Feature Selection
Pinterest San Francisco, CA, USA
SelectArena: A Modern Benchmark for Feature Selection on Real-world Tabular Data
Oct 9, 2026Bastian Schäfer, Dominika Matus, Lennart Purucker +5Feature Selection Benchmark Design
Université du Littoral-Côte-d’Opale · University of Freiburg · Prior Labs +2
$L_0$ Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression
Oct 9, 2026Cameron Mouangue, Tristan CazenaveFeature Selection Quantitative Finance
LAMSADE, Université Paris Dauphine-PSL, CNRS Paris, France
High-Dimensional Statistical Inference for Sparse Support Vector Machines
Oct 6, 2026Peng Zeng, Hanwen HuangSupport Vector Machines Feature Selection
Department of Mathematics and Statistics Auburn University, Auburn, AL 36849 · Department of Biostatistics, Data Science and Epidemiology Medical College of Georgia, Augusta University, Augusta, GA, 30912
LinearPFN: Amortized Variable Selection for Linear Models with Interactions
Oct 5, 2026Louis Schiekiera, Max Zimmer, Christophe Roux +3Model Selection Prior-Data Fitted Networks
Institute of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany · Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany · Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Germany +1
DePICT: Decision-Preserving Interface for Constrained Downstream Tasks
Oct 2, 2026Utkarsh Grover, Ravi Ranjan, Agoritsa Polyzou +4Predict-Then-Optimize Constrained Optimization
University of South Florida · Florida International University · DEVCOM Army Research Lab
Cross-cohort TB classification using clinical data gathered in Uganda and South Africa
Oct 2, 2026Joshua M. Jansen van Vüren, Devendra S. Parihar, Daphne Naidoo +8Medical Diagnosis Clinical Prediction
Department of Electrical and Electronic Engineering, University of Stellenbosch, Stellenbosch, South Africa · South African Medical Research Council Centre for Tuberculosis Research (CTR) Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences Stellenbosch University, Cape Town, South Africa · Department of Global and Amsterdam Institute for Global Health and Development Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands +2
Landscape-Dependent Performance of Photonic Quantum Solvers in QUBO Feature Selection for Financial Risk Detection
Oct 2, 2026Nirvik Sahoo, Paul Robert GriffinFeature Selection Photonic Computing
Singapore Management University 80 Stamford Road, Singapore 178902
Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models
Oct 2, 2026Shaïn Afzali, Serena Della Corte, Antonis PapapantoleonModel Calibration Feature Selection
A Multi Method Importance and Performance Efficiency Analysis of Topological Metrics for Natural Visibility Graph Based Cyber Attack Detection
Oct 1, 2026Ali Melih Kanca, Ilker TurkerNetwork Intrusion Detection Feature Selection
Department of Computer Engineering, Faculty of Computing and Informatics Sciences, Karabuk University, Karabuk, Türkiye
When Does Exercise-Specific Joint Selection Help? An Audit of Evaluation and Control Design
Oct 1, 2026Haotian Chen, Jingkun Yu, Yuning Zhang +1Skeleton-Based Action Recognition Classification
School of Cyber Science and Technology, University of Science and Technology of China · SWJTU-Leeds Joint School, Southwest Jiaotong University · School of Education, Shanghai Jiao Tong University
Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions
Sep 29, 2026Daniela De Canditiis, Italia De Feis, Paola StolfiRobust Loss Functions Feature Selection
Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, Via dei Taurini, 19, Roma, 00185, Italia · Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, Via Pietro Castellino, 111, Napoli, 80131, Italia
Architecture Alignment With Sparse Priors in Tabular Foundation Models
Sep 29, 2026Tianqi Zhao, Tianyi Zhuang, Shuo Duan +3Tabular Foundation Models Transformer
National University of Singapore · Rutgers University
Generative Uncertainty as a Self-supervised Signal for Semantic Similarity Learning
Sep 28, 2026Enrico Pallotta, Sina Raoufi, Lars Doorenbos +2Video Representation Learning Feature Selection
University of Bonn · Lamarr Institute for ML & AI · ENSTA Paris, Institut Polytechnique de Paris
Sparsity by Default: The Theory and Practice of ARD in Gaussian Process Regression for Variable Selection
Sep 27, 2026Jia CaiFeature Selection Gaussian Process Regression
Department of Statistics, George Mason University, Fairfax, Virginia/USA
Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection
Sep 27, 2026Jia CaiFDR Control Feature Selection
Department of Statistics, George Mason University Fairfax, Virginia, USA
Rethinking Cross-Channel Importance in Time-Series Forecasting
Sep 26, 2026Yong-Hoon Choi, Kwang-Hyun Park, Youngjin ChoMultivariate Time Series Forecasting Explainable Time Series Forecasting
Division of Robotics, Kwangwoon University Seoul 01897, Republic of Korea
On Basis Function Selection for Sparse Gaussian Process Regression
Sep 22, 2026Marnix Van Soom, Ivan De BoiFeature Selection Sparse Gaussian Processes
AI Lab Vrije Universiteit Brussel · InViLab University of Antwerp

Oct 22, 2026J/K move · Enter open · S save

Post-Hoc Feature Selection Layer for Neural Networks Interpretability

João Kenji Suwa, João Pedro Silveira e Silva, Bruno Iochins Grisci

Institute of Informatics, UFRGS, Porto Alegre, Brazil

Neural Network Interpretability Feature Attribution Feature Selection

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The interpretability of complex neural networks remains a critical challenge, especially for models already deployed in high-stakes domains. To address this, we adapted the Feature Selection Layer (FSL) to optimize feature weights post-training, a variant we term post-hoc FSL. Our approach reframes the FSL as a lightweight, trainable module that integrates with already frozen pre-trained models on tabular datasets to highlight the features the original model considers most important. This post-hoc FSL learns feature relevance by fine-tuning its weights based on the original model's learned outputs. Crucially, this process is non-invasive, operating without altering the original model's architecture or its learned parameters. We conducted our experiments using both statistical and visual metrics, including accuracy, F1 score, recall, precision, weighted t-SNE and silhouette score, and also analyzed the stability of the post-hoc FSL on high-dimensional synthetic and real-world tabular datasets. We compared the post-hoc FSL feature weighting method using these metrics against the original embedded FSL and other post-hoc interpretability methods, such as Integrated Gradients, Noise Tunnel, DeepLIFT, Gradient SHAP, and Feature Ablation. Experimental results demonstrate that the post-hoc FSL feature weighting method successfully identified relevant features across the different datasets, maintaining the predictive power of the original neural network while enhancing its interpretability.