{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/feature-selection/papers/8","list_of":"/task/feature-selection","task":"feature selection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":8,"pages_in_order":30,"rows_per_page":100,"rows":[701,800],"of":2971,"counts":{"archive_papers_tagged":2971,"with_a_code_link":707,"where_syntology_ran_a_sample":85,"not_listed_spam_title":0,"listed":2971,"listed_where_code_ran":85,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":71,"every_run_a_failure_of_syntologys_instrument":14,"listed_with_a_run_with_no_instrument_failure":71,"listed_every_run_a_failure_of_syntologys_instrument":14,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/feature-selection","prev":"/task/feature-selection/papers/7","next":"/task/feature-selection/papers/9","papers":[{"url":"/paper/auto-weka-combined-selection-and","slug":"auto-weka-combined-selection-and","title":"Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms","date":"2012-08-18","arxiv_id":"1208.3719","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-fisher-score-for-feature","slug":"generalized-fisher-score-for-feature","title":"Generalized Fisher Score for Feature Selection","date":"2012-02-14","arxiv_id":"1202.3725","repositories_listed":1,"syntology":null},{"url":"/paper/feature-selection-with-the-boruta-package","slug":"feature-selection-with-the-boruta-package","title":"Feature Selection with the Boruta Package","date":"2010-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/condition-number-analysis-of-kernel-based","slug":"condition-number-analysis-of-kernel-based","title":"Condition Number Analysis of Kernel-based Density Ratio Estimation","date":"2009-12-15","arxiv_id":"0912.2800","repositories_listed":1,"syntology":null},{"url":"/paper/feature-selection-in-omics-prediction","slug":"feature-selection-in-omics-prediction","title":"Feature selection in omics prediction problems using cat scores and false nondiscovery rate control","date":"2009-03-11","arxiv_id":"0903.2003","repositories_listed":1,"syntology":null},{"url":"/paper/feature-selection-based-on-mutual-information","slug":"feature-selection-based-on-mutual-information","title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","date":"2005-06-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cached-sufficient-statistics-for-efficient","slug":"cached-sufficient-statistics-for-efficient","title":"Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets","date":"1998-03-01","arxiv_id":"cs/9803102","repositories_listed":1,"syntology":null},{"url":null,"slug":"mnarx-a-surrogate-model-for-complex-dynamical","title":"mNARX+: A surrogate model for complex dynamical systems using manifold-NARX and automatic feature selection","date":"2025-07-17","arxiv_id":"2507.13301","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-model-for-poultry-disease","title":"Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning","date":"2025-07-14","arxiv_id":"2507.10056","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-motion-to-meaning-biomechanics-informed","title":"From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification","date":"2025-07-08","arxiv_id":"2507.05783","repositories_listed":0,"syntology":null},{"url":null,"slug":"scmamba-a-scalable-foundation-model-for","title":"scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection","date":"2025-06-25","arxiv_id":"2506.20697","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-interpretable-and-efficient-feature","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","date":"2025-06-25","arxiv_id":"2506.20359","repositories_listed":0,"syntology":null},{"url":null,"slug":"vulnerability-disclosure-through-adaptive","title":"Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS","date":"2025-06-25","arxiv_id":"2506.20576","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-malware-detection-with-optimized","title":"Efficient Malware Detection with Optimized Learning on High-Dimensional Features","date":"2025-06-18","arxiv_id":"2506.17309","repositories_listed":0,"syntology":null},{"url":null,"slug":"condition-monitoring-with-machine-learning-a","title":"Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss","date":"2025-06-16","arxiv_id":"2506.13012","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lightweight-ids-for-early-apt-detection","title":"A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method","date":"2025-06-13","arxiv_id":"2506.12108","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-10929","title":"On feature selection in double-imbalanced data settings: a Random Forest approach","date":"2025-06-12","arxiv_id":"2506.10929","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-oral-cancer-outcomes-through","title":"Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction","date":"2025-06-11","arxiv_id":"2506.10189","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-genetic-algorithms-with-multilayer","title":"Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition","date":"2025-06-11","arxiv_id":"2506.10184","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08059","title":"CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction","date":"2025-06-09","arxiv_id":"2506.08059","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08047","title":"Evaluation of Machine Learning Models in Student Academic Performance Prediction","date":"2025-06-08","arxiv_id":"2506.08047","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-resistant-label-reconstruction-feature","title":"Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning","date":"2025-06-05","arxiv_id":"2506.04669","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-analysis-in-learning-management","title":"Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale","date":"2025-06-05","arxiv_id":"2506.05490","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-dental-care-providers-through","title":"Classifying Dental Care Providers Through Machine Learning with Features Ranking","date":"2025-06-04","arxiv_id":"2506.04474","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-power-demand-forecasting-for","title":"Short-Term Power Demand Forecasting for Diverse Consumer Types to Enhance Grid Planning and Synchronisation","date":"2025-06-04","arxiv_id":"2506.04294","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-postoperative-stroke-in-elderly","title":"Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data","date":"2025-06-02","arxiv_id":"2506.03209","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-cognition-machine-learning-for","title":"Quantum Cognition Machine Learning for Forecasting Chromosomal Instability","date":"2025-06-02","arxiv_id":"2506.03199","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-review-of-metaheuristics-based","title":"A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT","date":"2025-05-31","arxiv_id":"2506.00377","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-shap-based-explainable-multi-level-stacking","title":"A SHAP-based explainable multi-level stacking ensemble learning method for predicting the length of stay in acute stroke","date":"2025-05-30","arxiv_id":"2505.24101","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-unlearning-via-sparse-autoencoder","title":"Model Unlearning via Sparse Autoencoder Subspace Guided Projections","date":"2025-05-30","arxiv_id":"2505.24428","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-random-walk-with-feature-label-space","title":"Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method","date":"2025-05-29","arxiv_id":"2505.23228","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-patterns-of-cognitive","title":"Identification of Patterns of Cognitive Impairment for Early Detection of Dementia","date":"2025-05-29","arxiv_id":"2505.23109","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-storytelling-improving-audience","title":"Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry","date":"2025-05-29","arxiv_id":"2506.00076","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-statistical-learning-methods-via","title":"Improving statistical learning methods via features selection without replacement sampling and random projection","date":"2025-05-28","arxiv_id":"2506.00053","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-chameleons-irrelevant-context","title":"Stochastic Chameleons: Irrelevant Context Hallucinations Reveal Class-Based (Mis)Generalization in LLMs","date":"2025-05-28","arxiv_id":"2505.22630","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-3d-channel-modeling-for-cellular","title":"Empirical 3D Channel Modeling for Cellular-Connected UAVs: A Triple-Layer Machine Learning Approach","date":"2025-05-26","arxiv_id":"2505.19478","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-interactive-nomograms-for","title":"Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia","date":"2025-05-23","arxiv_id":"2505.18421","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-step-comparative-framework-for","title":"A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams","date":"2025-05-22","arxiv_id":"2505.16872","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-scalable-hierarchical-intrusion-detection","title":"A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles","date":"2025-05-22","arxiv_id":"2505.16215","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-trustworthy-keylogger-detection-a","title":"Towards Trustworthy Keylogger detection: A Comprehensive Analysis of Ensemble Techniques and Feature Selections through Explainable AI","date":"2025-05-22","arxiv_id":"2505.16103","repositories_listed":0,"syntology":null},{"url":null,"slug":"agentic-feature-augmentation-unifying","title":"Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories","date":"2025-05-21","arxiv_id":"2505.15076","repositories_listed":0,"syntology":null},{"url":null,"slug":"margin-aware-fuzzy-rough-feature-selection","title":"Margin-aware Fuzzy Rough Feature Selection: Bridging Uncertainty Characterization and Pattern Classification","date":"2025-05-21","arxiv_id":"2505.15250","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-close-shannon-entropy-approximation","title":"Fast and close Shannon entropy approximation","date":"2025-05-20","arxiv_id":"2505.14234","repositories_listed":0,"syntology":null},{"url":null,"slug":"pathobiological-dictionary-defining-pathomics","title":"Pathobiological Dictionary Defining Pathomics and Texture Features: Addressing Understandable AI Issues in Personalized Liver Cancer; Dictionary Version LCP1.0","date":"2025-05-20","arxiv_id":"2505.14926","repositories_listed":0,"syntology":null},{"url":null,"slug":"sae-fire-enhancing-earnings-surprise","title":"SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection","date":"2025-05-20","arxiv_id":"2505.14420","repositories_listed":0,"syntology":null},{"url":null,"slug":"streamlining-http-flooding-attack-detection","title":"Streamlining HTTP Flooding Attack Detection through Incremental Feature Selection","date":"2025-05-20","arxiv_id":"2505.17077","repositories_listed":0,"syntology":null},{"url":null,"slug":"outsourced-privacy-preserving-feature","title":"Outsourced Privacy-Preserving Feature Selection Based on Fully Homomorphic Encryption","date":"2025-05-19","arxiv_id":"2505.12869","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-tabular-stroke-modelling-through-a","title":"Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy","date":"2025-05-18","arxiv_id":"2505.15844","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-silico-tool-for-identification-of","title":"In silico tool for identification of colorectal cancer from cell-free DNA biomarkers","date":"2025-05-16","arxiv_id":"2505.11041","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-10600","title":"Enhancing IoT Cyber Attack Detection in the Presence of Highly Imbalanced Data","date":"2025-05-15","arxiv_id":"2505.10600","repositories_listed":0,"syntology":null},{"url":null,"slug":"financial-fraud-detection-using-explainable","title":"Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods","date":"2025-05-15","arxiv_id":"2505.10050","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmenting-the-availability-of-historical-gdp","title":"Augmenting the availability of historical GDP per capita estimates through machine learning","date":"2025-05-14","arxiv_id":"2505.09399","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-nonlinear-pdes-with-sparse-radial","title":"Solving Nonlinear PDEs with Sparse Radial Basis Function Networks","date":"2025-05-12","arxiv_id":"2505.07765","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-high-dimensional-feature-selection","title":"A High-Dimensional Feature Selection Algorithm Based on Multiobjective Differential Evolution","date":"2025-05-09","arxiv_id":"2505.05727","repositories_listed":0,"syntology":null},{"url":null,"slug":"voice-biomarkers-of-perinatal-depression","title":"Voice biomarkers of perinatal depression: cross-sectional nationwide pilot study report","date":"2025-05-09","arxiv_id":"2505.06412","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-risks-and-regulatory-strategies","title":"Personalized Risks and Regulatory Strategies of Large Language Models in Digital Advertising","date":"2025-05-07","arxiv_id":"2505.04665","repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-juntas-optimally-with-samples","title":"Testing Juntas Optimally with Samples","date":"2025-05-07","arxiv_id":"2505.04604","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graphical-global-optimization-framework-for","title":"A Graphical Global Optimization Framework for Parameter Estimation of Statistical Models with Nonconvex Regularization Functions","date":"2025-05-06","arxiv_id":"2505.03899","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-omics-based-classification-the-role","title":"Improving Omics-Based Classification: The Role of Feature Selection and Synthetic Data Generation","date":"2025-05-06","arxiv_id":"2505.03387","repositories_listed":0,"syntology":null},{"url":"/paper/sand-one-shot-feature-selection-with-additive","slug":"sand-one-shot-feature-selection-with-additive","title":"SAND: One-Shot Feature Selection with Additive Noise Distortion","date":"2025-05-06","arxiv_id":"2505.03923","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/sand-one-shot-feature-selection-with-additive#ran","syntology_url":"https://syntology.ai/paper/2505.03923","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.03923"}},"official":null}},{"url":null,"slug":"fast2comm-collaborative-perception-combined","title":"Fast2comm:Collaborative perception combined with prior knowledge","date":"2025-04-30","arxiv_id":"2505.00740","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-mixed-linear-modeling-with-anchor","title":"Sparse mixed linear modeling with anchor-based guidance for high-entropy alloy discovery","date":"2025-04-29","arxiv_id":"2504.20354","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-transfer-learning-statistical-inference","title":"Post-Transfer Learning Statistical Inference in High-Dimensional Regression","date":"2025-04-25","arxiv_id":"2504.18212","repositories_listed":0,"syntology":null},{"url":null,"slug":"fishing-for-phishers-learning-based-phishing","title":"Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions","date":"2025-04-24","arxiv_id":"2504.17953","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-approaches-to-malware-detection-a","title":"Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques","date":"2025-04-24","arxiv_id":"2504.17930","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-based-raman-spectral-processing","title":"A Graph Based Raman Spectral Processing Technique for Exosome Classification","date":"2025-04-21","arxiv_id":"2504.15324","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-via-gans-ganfs-enhancing","title":"Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation","date":"2025-04-21","arxiv_id":"2504.18566","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-encoder-and-multi-features","title":"Transformer Encoder and Multi-features Time2Vec for Financial Prediction","date":"2025-04-18","arxiv_id":"2504.13801","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-based-on-cluster-assumption","title":"Feature selection based on cluster assumption in PU learning","date":"2025-04-17","arxiv_id":"2504.12651","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpmfs-global-foundation-and-personalized","title":"GPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection","date":"2025-04-17","arxiv_id":"2504.12740","repositories_listed":0,"syntology":null},{"url":null,"slug":"ihho-smote-a-cleansed-approach-for-handling","title":"iHHO-SMOTe: A Cleansed Approach for Handling Outliers and Reducing Noise to Improve Imbalanced Data Classification","date":"2025-04-17","arxiv_id":"2504.12850","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepselective-feature-gating-and","title":"DeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction","date":"2025-04-15","arxiv_id":"2504.11264","repositories_listed":0,"syntology":null},{"url":null,"slug":"icafs-inter-client-aware-feature-selection","title":"ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning","date":"2025-04-15","arxiv_id":"2504.10851","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-vision-and-location-with","title":"Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis","date":"2025-04-14","arxiv_id":"2504.10452","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-modality-disruption-in-multimodal","title":"Exploring Modality Disruption in Multimodal Fake News Detection","date":"2025-04-12","arxiv_id":"2504.09154","repositories_listed":0,"syntology":null},{"url":null,"slug":"saes-textit-can-improve-unlearning-dynamic","title":"SAEs $\\textit{Can}$ Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs","date":"2025-04-11","arxiv_id":"2504.08192","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-deep-learning-approach-for-non-invasive","title":"Dual Deep Learning Approach for Non-invasive Renal Tumour Subtyping with VERDICT-MRI","date":"2025-04-09","arxiv_id":"2504.07246","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-regions-of-interest-in-whole","title":"Identifying regions of interest in whole slide images of renal cell carcinoma","date":"2025-04-09","arxiv_id":"2504.07313","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedded-federated-feature-selection-with","title":"Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs","date":"2025-04-07","arxiv_id":"2504.05245","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-feature-selection-and-neural","title":"Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals","date":"2025-04-04","arxiv_id":"2504.03984","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-cloud-objective-quality-benchmarking","title":"Point Cloud Objective Quality: Benchmarking Features and Quality Evaluation","date":"2025-04-04","arxiv_id":"2504.03381","repositories_listed":0,"syntology":null},{"url":null,"slug":"fourier-feature-attribution-a-new-efficiency","title":"Fourier Feature Attribution: A New Efficiency Attribution Method","date":"2025-04-02","arxiv_id":"2504.02016","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-variability-and-radiomics","title":"Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging","date":"2025-04-02","arxiv_id":"2504.01692","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-to-event-prediction-for-grouped","title":"Time-to-event prediction for grouped variables using Exclusive Lasso","date":"2025-04-02","arxiv_id":"2504.01520","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-stroke-disease-classification","title":"Enhancing stroke disease classification through machine learning models by feature selection techniques","date":"2025-04-01","arxiv_id":"2504.00485","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigation-of-intelligent-barbell-squat","title":"Investigation of intelligent barbell squat coaching system based on computer vision and machine learning","date":"2025-03-31","arxiv_id":"2503.23731","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm4fs-leveraging-large-language-models-for","title":"LLM4FS: Leveraging Large Language Models for Feature Selection and How to Improve It","date":"2025-03-31","arxiv_id":"2503.24157","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-domain-specific-knowledge-from-llms","title":"Embedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline","date":"2025-03-27","arxiv_id":"2503.21155","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-using-computer-vision-a","title":"Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics","date":"2025-03-24","arxiv_id":"2503.19100","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-feature-interaction-via","title":"Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data","date":"2025-03-23","arxiv_id":"2503.18048","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-strategies-for-optimized","title":"Feature selection strategies for optimized heart disease diagnosis using ML and DL models","date":"2025-03-20","arxiv_id":"2503.16577","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-unmixing-using-iterative-sparse","title":"Hyperspectral Unmixing using Iterative, Sparse and Ensambling Approaches for Large Spectral Libraries Applied to Soils and Minerals","date":"2025-03-20","arxiv_id":"2503.16298","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-powered-prediction-of-nanoparticle","title":"AI-Powered Prediction of Nanoparticle Pharmacokinetics: A Multi-View Learning Approach","date":"2025-03-18","arxiv_id":"2503.13798","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-feature-selection-based-on-binary","title":"Multi-label feature selection based on binary hashing learning and dynamic graph constraints","date":"2025-03-18","arxiv_id":"2503.13874","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-global-view-reconstruction","title":"Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection","date":"2025-03-18","arxiv_id":"2503.14024","repositories_listed":0,"syntology":null},{"url":null,"slug":"cardiomyopathy-diagnosis-model-from","title":"Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data","date":"2025-03-14","arxiv_id":"2503.11331","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconsidering-feature-structure-information","title":"Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection","date":"2025-03-13","arxiv_id":"2503.10115","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-feature-selection-from-variable","title":"Dynamic Feature Selection from Variable Feature Sets Using Features of Features","date":"2025-03-12","arxiv_id":"2503.09181","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-feature-selection-for-predicting","title":"Effective Feature Selection for Predicting Spreading Factor with ML in Large LoRaWAN-based Mobile IoT Networks","date":"2025-03-12","arxiv_id":"2503.09170","repositories_listed":0,"syntology":null},{"url":null,"slug":"tabnsa-native-sparse-attention-for-efficient","title":"TabNSA: Native Sparse Attention for Efficient Tabular Data Learning","date":"2025-03-12","arxiv_id":"2503.09850","repositories_listed":0,"syntology":null}],"record_sha256":"d8d1c70332751860e202379ad87de5ce7bd4096daeb8ef503e86151d9f844098","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}