{"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":"/method/feature-selection/papers/4","list_of":"/method/feature-selection","method":"Feature Selection","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":4,"pages_in_order":17,"rows_per_page":100,"rows":[301,400],"of":1602,"counts":{"archive_papers_tagged":1602,"with_a_code_link":435,"where_syntology_ran_a_sample":52,"not_listed_spam_title":0,"listed":1602,"listed_where_code_ran":52,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":46,"every_run_a_failure_of_syntologys_instrument":6,"listed_with_a_run_with_no_instrument_failure":46,"listed_every_run_a_failure_of_syntologys_instrument":6,"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":"/method/feature-selection","prev":"/method/feature-selection/papers/3","next":"/method/feature-selection/papers/5","papers":[{"paper":null,"slug":"a-data-balancing-approach-designing-of-an","title":"A data balancing approach towards design of an expert system for Heart Disease Prediction","date":"2024-07-26","arxiv_id":"2407.18606","n_code_links":0,"syntology":null},{"paper":null,"slug":"capturing-the-security-expert-knowledge-in","title":"Capturing the security expert knowledge in feature selection for web application attack detection","date":"2024-07-26","arxiv_id":"2407.18445","n_code_links":0,"syntology":null},{"paper":null,"slug":"utilising-explainable-techniques-for-quality","title":"Utilising Explainable Techniques for Quality Prediction in a Complex Textiles Manufacturing Use Case","date":"2024-07-26","arxiv_id":"2407.18544","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-vendor-reproducibility-of-radiomics","title":"Cross-Vendor Reproducibility of Radiomics-based Machine Learning Models for Computer-aided Diagnosis","date":"2024-07-25","arxiv_id":"2407.18060","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-diversity-in-multi-objective","title":"Enhancing Diversity in Multi-objective Feature Selection","date":"2024-07-25","arxiv_id":"2407.17795","n_code_links":0,"syntology":null},{"paper":null,"slug":"coeff-kans-a-paradigm-to-address-the","title":"COEFF-KANs: A Paradigm to Address the Electrolyte Field with KANs","date":"2024-07-24","arxiv_id":"2407.20265","n_code_links":0,"syntology":null},{"paper":"/paper/sparse-tensor-pca-via-tensor-decomposition","slug":"sparse-tensor-pca-via-tensor-decomposition","title":"Sparse Tensor PCA via Tensor Decomposition for Unsupervised Feature Selection","date":"2024-07-24","arxiv_id":"2407.16985","n_code_links":1,"syntology":null},{"paper":null,"slug":"development-of-multistage-machine-learning","title":"Development of Multistage Machine Learning Classifier using Decision Trees and Boosting Algorithms over Darknet Network Traffic","date":"2024-07-22","arxiv_id":"2407.15910","n_code_links":0,"syntology":null},{"paper":"/paper/distance-based-mutual-congestion-feature","slug":"distance-based-mutual-congestion-feature","title":"Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets","date":"2024-07-22","arxiv_id":"2407.15611","n_code_links":1,"syntology":null},{"paper":null,"slug":"hiervar-a-hierarchical-feature-selection","title":"HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis","date":"2024-07-22","arxiv_id":"2407.16048","n_code_links":0,"syntology":null},{"paper":null,"slug":"revisiting-score-function-estimators-for-k","title":"Revisiting Score Function Estimators for $k$-Subset Sampling","date":"2024-07-22","arxiv_id":"2407.16058","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhanced-mortality-prediction-in-icu-stroke","title":"Advanced Predictive Modeling for Enhanced Mortality Prediction in ICU Stroke Patients Using Clinical Data","date":"2024-07-19","arxiv_id":"2407.14211","n_code_links":0,"syntology":null},{"paper":null,"slug":"two-new-feature-selection-methods-based-on","title":"Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: A comprehensive analysis","date":"2024-07-19","arxiv_id":"2407.14631","n_code_links":0,"syntology":null},{"paper":null,"slug":"discussion-effective-and-interpretable","title":"Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts","date":"2024-07-18","arxiv_id":"2407.13526","n_code_links":0,"syntology":null},{"paper":"/paper/global-local-similarity-for-efficient-fine","slug":"global-local-similarity-for-efficient-fine","title":"Global-Local Similarity for Efficient Fine-Grained Image Recognition with Vision Transformers","date":"2024-07-17","arxiv_id":"2407.12891","n_code_links":1,"syntology":null},{"paper":"/paper/word-embedding-dimension-reduction-via-weakly","slug":"word-embedding-dimension-reduction-via-weakly","title":"Word Embedding Dimension Reduction via Weakly-Supervised Feature Selection","date":"2024-07-17","arxiv_id":"2407.12342","n_code_links":1,"syntology":null},{"paper":"/paper/local-feature-selection-without-label-or","slug":"local-feature-selection-without-label-or","title":"Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions","date":"2024-07-16","arxiv_id":"2407.11778","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["garfieldlyu/suwr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/thyroidiomics-an-automated-pipeline-for","slug":"thyroidiomics-an-automated-pipeline-for","title":"Thyroidiomics: An Automated Pipeline for Segmentation and Classification of Thyroid Pathologies from Scintigraphy Images","date":"2024-07-14","arxiv_id":"2407.10336","n_code_links":1,"syntology":null},{"paper":null,"slug":"pso-fuzzy-xgboost-classifier-boosted-with","title":"PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition","date":"2024-07-13","arxiv_id":"2407.09950","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-ai-for-enhancing-efficiency-of-dl","title":"Explainable AI for Enhancing Efficiency of DL-based Channel Estimation","date":"2024-07-09","arxiv_id":"2407.07009","n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-speech-unit-selection-for-textless","title":"Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation","date":"2024-07-08","arxiv_id":"2407.18332","n_code_links":0,"syntology":null},{"paper":"/paper/brainmetdetect-predicting-primary-tumor-from","slug":"brainmetdetect-predicting-primary-tumor-from","title":"BrainMetDetect: Predicting Primary Tumor from Brain Metastasis MRI Data Using Radiomic Features and Machine Learning Algorithms","date":"2024-07-06","arxiv_id":"2407.05051","n_code_links":1,"syntology":null},{"paper":null,"slug":"lednet-localization-enabled-deep-neural","title":"LeDNet: Localization-enabled Deep Neural Network for Multi-Label Radiography Image Classification","date":"2024-07-04","arxiv_id":"2407.03931","n_code_links":0,"syntology":null},{"paper":null,"slug":"cruise-on-quantum-computing-for-feature","title":"CRUISE on Quantum Computing for Feature Selection in Recommender Systems","date":"2024-07-03","arxiv_id":"2407.02839","n_code_links":0,"syntology":null},{"paper":null,"slug":"iris-and-palmprint-multimodal-biometric","title":"Impact of Financial Literacy on Investment Decisions and Stock Market Participation using Extreme Learning Machines","date":"2024-07-03","arxiv_id":"2407.03498","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-select-feature-selection-with-large","title":"LLM-Select: Feature Selection with Large Language Models","date":"2024-07-02","arxiv_id":"2407.02694","n_code_links":0,"syntology":null},{"paper":"/paper/statistical-test-for-data-analysis-pipeline","slug":"statistical-test-for-data-analysis-pipeline","title":"Statistical Test for Feature Selection Pipelines by Selective Inference","date":"2024-06-27","arxiv_id":"2406.18902","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-review-of-feature-selection-strategies","title":"A review of feature selection strategies utilizing graph data structures and knowledge graphs","date":"2024-06-21","arxiv_id":"2406.14864","n_code_links":0,"syntology":null},{"paper":null,"slug":"fair-streaming-feature-selection","title":"Fair Streaming Feature Selection","date":"2024-06-20","arxiv_id":"2406.14401","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-hybrid-intelligent-system-for-protection-of","title":"A Hybrid Intelligent System for Protection of Transmission Lines Connected to PV Farms based on Linear Trends","date":"2024-06-19","arxiv_id":"2406.13194","n_code_links":0,"syntology":null},{"paper":null,"slug":"network-community-analysis-of-cellular","title":"Network-community analysis of cellular senescence","date":"2024-06-19","arxiv_id":"2406.13889","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-collaborative-correlation-learning","title":"Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection","date":"2024-06-18","arxiv_id":"2406.12193","n_code_links":0,"syntology":null},{"paper":null,"slug":"stackelberg-games-with-k-submodular-function","title":"$k$-Submodular Interdiction Problems under Distributional Risk-Receptiveness and Robustness: Application to Machine Learning","date":"2024-06-18","arxiv_id":"2406.13023","n_code_links":0,"syntology":null},{"paper":null,"slug":"matching-query-image-against-selected-nerf","title":"Matching Query Image Against Selected NeRF Feature for Efficient and Scalable Localization","date":"2024-06-17","arxiv_id":"2406.11766","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-rate-emphasized-multi-objective","title":"Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection","date":"2024-06-13","arxiv_id":"2406.09180","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-adversarial-robustness-via-feature","title":"Improving Adversarial Robustness via Feature Pattern Consistency Constraint","date":"2024-06-13","arxiv_id":"2406.08829","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-network-traffic-feature-sets-for","title":"Efficient Network Traffic Feature Sets for IoT Intrusion Detection","date":"2024-06-12","arxiv_id":"2406.08042","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-limitation-of-kernel-dependence","title":"On the Limitation of Kernel Dependence Maximization for Feature Selection","date":"2024-06-11","arxiv_id":"2406.06903","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-dynamics-of-nonlinear-contrastive","title":"Training Dynamics of Nonlinear Contrastive Learning Model in the High Dimensional Limit","date":"2024-06-11","arxiv_id":"2406.06909","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-beamforming-feedback-information","title":"Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection","date":"2024-06-09","arxiv_id":"2406.05671","n_code_links":0,"syntology":null},{"paper":null,"slug":"development-and-validation-of-a-deep-learning-3","title":"Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study","date":"2024-06-07","arxiv_id":"2406.04993","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-feature-selection-methods-for","title":"Evaluating Feature Selection Methods for Macro-Economic Forecasting, Applied for Inflation Indicator of Iran","date":"2024-06-06","arxiv_id":"2406.03742","n_code_links":0,"syntology":null},{"paper":null,"slug":"sensitivity-assessing-to-data-volume-for","title":"Sensitivity Assessing to Data Volume for forecasting: introducing similarity methods as a suitable one in Feature selection methods","date":"2024-06-06","arxiv_id":"2406.04390","n_code_links":0,"syntology":null},{"paper":"/paper/giving-each-task-what-it-needs-leveraging","slug":"giving-each-task-what-it-needs-leveraging","title":"Giving each task what it needs -- leveraging structured sparsity for tailored multi-task learning","date":"2024-06-05","arxiv_id":"2406.03048","n_code_links":1,"syntology":null},{"paper":"/paper/ctrsvdd-a-benchmark-dataset-and-baseline","slug":"ctrsvdd-a-benchmark-dataset-and-baseline","title":"CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection","date":"2024-06-04","arxiv_id":"2406.02438","n_code_links":2,"syntology":null},{"paper":null,"slug":"reweighted-solutions-for-weighted-low-rank","title":"Reweighted Solutions for Weighted Low Rank Approximation","date":"2024-06-04","arxiv_id":"2406.02431","n_code_links":0,"syntology":null},{"paper":null,"slug":"conditional-gumbel-softmax-for-constrained","title":"Conditional Gumbel-Softmax for constrained feature selection with application to node selection in wireless sensor networks","date":"2024-06-03","arxiv_id":"2406.01162","n_code_links":0,"syntology":null},{"paper":null,"slug":"gated-recurrent-neural-network-with-tpe","title":"Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy","date":"2024-06-02","arxiv_id":"2406.02604","n_code_links":0,"syntology":null},{"paper":"/paper/interpretabnet-distilling-predictive-signals","slug":"interpretabnet-distilling-predictive-signals","title":"InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation","date":"2024-06-01","arxiv_id":"2406.00426","n_code_links":1,"syntology":{"ran":10,"of":15,"n_ran_checked":10,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"10 ran (of which 10 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified; every one of the 10 samples that ran constructed an object rather than computing a result","official":{"repos":["jacobyhsi/InterpreTabNet"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":10,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/unveiling-hidden-factors-explainable-ai-for","slug":"unveiling-hidden-factors-explainable-ai-for","title":"Unveiling Hidden Factors: Explainable AI for Feature Boosting in Speech Emotion Recognition","date":"2024-06-01","arxiv_id":"2406.01624","n_code_links":2,"syntology":null},{"paper":"/paper/analysis-of-clinical-dosimetric-and-radiomic","slug":"analysis-of-clinical-dosimetric-and-radiomic","title":"Analysis of clinical, dosimetric and radiomic features for predicting local failure after stereotactic radiotherapy of brain metastases in malignant melanoma","date":"2024-05-31","arxiv_id":"2405.20825","n_code_links":1,"syntology":null},{"paper":null,"slug":"superfast-selection-for-decision-tree","title":"Superfast Selection for Decision Tree Algorithms","date":"2024-05-31","arxiv_id":"2405.20622","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-feature-selection-in-medical","title":"Dynamic feature selection in medical predictive monitoring by reinforcement learning","date":"2024-05-30","arxiv_id":"2405.19729","n_code_links":0,"syntology":null},{"paper":"/paper/iterative-feature-boosting-for-explainable-1","slug":"iterative-feature-boosting-for-explainable-1","title":"Iterative Feature Boosting for Explainable Speech Emotion Recognition","date":"2024-05-30","arxiv_id":"2405.20172","n_code_links":1,"syntology":null},{"paper":null,"slug":"texture-guided-coding-for-deep-features","title":"Texture-guided Coding for Deep Features","date":"2024-05-30","arxiv_id":"2405.19669","n_code_links":0,"syntology":null},{"paper":"/paper/conformal-recursive-feature-elimination","slug":"conformal-recursive-feature-elimination","title":"Conformal Recursive Feature Elimination","date":"2024-05-29","arxiv_id":"2405.19429","n_code_links":1,"syntology":null},{"paper":null,"slug":"goi-find-3d-gaussians-of-interest-with-an","title":"GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane","date":"2024-05-27","arxiv_id":"2405.17596","n_code_links":0,"syntology":null},{"paper":"/paper/decomposing-the-neurons-activation-sparsity","slug":"decomposing-the-neurons-activation-sparsity","title":"Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation","date":"2024-05-26","arxiv_id":"2405.16486","n_code_links":1,"syntology":{"ran":12,"of":15,"n_ran_checked":10,"n_instrument":2,"unverified":3,"pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 1 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["royzry98/moase-pytorch"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/prediction-of-naloxone-dose-in-opioids","slug":"prediction-of-naloxone-dose-in-opioids","title":"Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence)","date":"2024-05-21","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/depth-linear-discrimination-oriented-feature","slug":"depth-linear-discrimination-oriented-feature","title":"Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction","date":"2024-05-20","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"an-autoencoder-and-generative-adversarial","title":"An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification","date":"2024-05-16","arxiv_id":"2405.09756","n_code_links":0,"syntology":null},{"paper":null,"slug":"dual-band-feature-selection-for-maturity","title":"Dual-band feature selection for maturity classification of specialty crops by hyperspectral imaging","date":"2024-05-16","arxiv_id":"2405.09955","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-driven-biomarker-selection","title":"Machine Learning Driven Biomarker Selection for Medical Diagnosis","date":"2024-05-16","arxiv_id":"2405.10345","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-sales-forecasts-through-automated","title":"Optimizing Sales Forecasts through Automated Integration of Market Indicators","date":"2024-05-15","arxiv_id":"2406.07564","n_code_links":0,"syntology":null},{"paper":"/paper/is-interpretable-machine-learning-effective","slug":"is-interpretable-machine-learning-effective","title":"Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?","date":"2024-05-13","arxiv_id":"2405.07782","n_code_links":1,"syntology":null},{"paper":null,"slug":"sparse-domain-transfer-via-elastic-net","title":"Sparse Domain Transfer via Elastic Net Regularization","date":"2024-05-13","arxiv_id":"2405.07489","n_code_links":0,"syntology":null},{"paper":"/paper/decoding-cognitive-health-using-machine","slug":"decoding-cognitive-health-using-machine","title":"Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern","date":"2024-05-11","arxiv_id":"2405.07070","n_code_links":1,"syntology":null},{"paper":"/paper/fingertip-video-dataset-for-non-invasive","slug":"fingertip-video-dataset-for-non-invasive","title":"Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier","date":"2024-05-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/guiding-adaptive-shrinkage-by-co-data-to","slug":"guiding-adaptive-shrinkage-by-co-data-to","title":"Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selection","date":"2024-05-08","arxiv_id":"2405.04917","n_code_links":1,"syntology":null},{"paper":"/paper/physics-based-deep-learning-reveals-rising","slug":"physics-based-deep-learning-reveals-rising","title":"Physics-based deep learning reveals rising heating demand heightens air pollution in Norwegian cities","date":"2024-05-07","arxiv_id":"2405.04716","n_code_links":1,"syntology":null},{"paper":null,"slug":"class-relevant-patch-embedding-selection-for","title":"Class-relevant Patch Embedding Selection for Few-Shot Image Classification","date":"2024-05-06","arxiv_id":"2405.03722","n_code_links":0,"syntology":null},{"paper":"/paper/a-multi-domain-multi-task-approach-for","slug":"a-multi-domain-multi-task-approach-for","title":"A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA Datasets","date":"2024-05-04","arxiv_id":"2405.02534","n_code_links":1,"syntology":null},{"paper":"/paper/sffnet-a-wavelet-based-spatial-and-frequency","slug":"sffnet-a-wavelet-based-spatial-and-frequency","title":"SFFNet: A Wavelet-Based Spatial and Frequency Domain Fusion Network for Remote Sensing Segmentation","date":"2024-05-03","arxiv_id":"2405.01992","n_code_links":1,"syntology":null},{"paper":null,"slug":"fmlfs-a-federated-multi-label-feature","title":"FMLFS: A Federated Multi-Label Feature Selection Based on Information Theory in IoT Environment","date":"2024-05-01","arxiv_id":"2405.00524","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-optimization-of-piecewise-linear","title":"Joint Optimization of Piecewise Linear Ensembles","date":"2024-05-01","arxiv_id":"2405.00303","n_code_links":0,"syntology":null},{"paper":"/paper/cbmap-clustering-based-manifold-approximation","slug":"cbmap-clustering-based-manifold-approximation","title":"CBMAP: Clustering-based manifold approximation and projection for dimensionality reduction","date":"2024-04-27","arxiv_id":"2404.17940","n_code_links":1,"syntology":null},{"paper":null,"slug":"feature-graphs-for-interpretable-unsupervised","title":"Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping","date":"2024-04-27","arxiv_id":"2404.17886","n_code_links":0,"syntology":null},{"paper":"/paper/fairgt-a-fairness-aware-graph-transformer","slug":"fairgt-a-fairness-aware-graph-transformer","title":"FairGT: A Fairness-aware Graph Transformer","date":"2024-04-26","arxiv_id":"2404.17169","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yushuowiki/fairgt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/neuro-symbolic-embedding-for-short-and","slug":"neuro-symbolic-embedding-for-short-and","title":"Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation","date":"2024-04-26","arxiv_id":"2404.17157","n_code_links":1,"syntology":null},{"paper":null,"slug":"automated-model-selection-for-generalized","title":"Automated Model Selection for Generalized Linear Models","date":"2024-04-25","arxiv_id":"2404.16560","n_code_links":0,"syntology":null},{"paper":"/paper/explainable-lightgbm-approach-for-predicting","slug":"explainable-lightgbm-approach-for-predicting","title":"Explainable LightGBM Approach for Predicting Myocardial Infarction Mortality","date":"2024-04-23","arxiv_id":"2404.15029","n_code_links":1,"syntology":null},{"paper":null,"slug":"interpretable-prediction-and-feature","title":"Interpretable Prediction and Feature Selection for Survival Analysis","date":"2024-04-23","arxiv_id":"2404.14689","n_code_links":0,"syntology":null},{"paper":"/paper/traditional-to-transformers-a-survey-on","slug":"traditional-to-transformers-a-survey-on","title":"A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models","date":"2024-04-23","arxiv_id":"2404.14955","n_code_links":1,"syntology":null},{"paper":null,"slug":"minimum-description-feature-selection-for","title":"Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning","date":"2024-04-21","arxiv_id":"2404.15374","n_code_links":0,"syntology":null},{"paper":"/paper/peach-pretrained-embedding-explanation-across","slug":"peach-pretrained-embedding-explanation-across","title":"PEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure","date":"2024-04-21","arxiv_id":"2404.13645","n_code_links":2,"syntology":null},{"paper":null,"slug":"lower-limb-movements-recognition-based-on","title":"Lower Limb Movements Recognition Based on Feature Recursive Elimination and Backpropagation Neural Network","date":"2024-04-17","arxiv_id":"2404.11383","n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-selection-in-linear-svms-via-hard","title":"Feature selection in linear SVMs via a hard cardinality constraint: a scalable SDP decomposition approach","date":"2024-04-15","arxiv_id":"2404.10099","n_code_links":0,"syntology":null},{"paper":"/paper/alice-combining-feature-selection-and-inter","slug":"alice-combining-feature-selection-and-inter","title":"ALICE: Combining Feature Selection and Inter-Rater Agreeability for Machine Learning Insights","date":"2024-04-13","arxiv_id":"2404.09053","n_code_links":1,"syntology":null},{"paper":"/paper/do-we-really-need-imputation-in-automl","slug":"do-we-really-need-imputation-in-automl","title":"Do We Really Need Imputation in AutoML Predictive Modeling?","date":"2024-04-12","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/deeplink-t-deep-learning-inference-for-time","slug":"deeplink-t-deep-learning-inference-for-time","title":"DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM","date":"2024-04-05","arxiv_id":"2404.04317","n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-genetic-algorithm-for-feature-selection","title":"Fast Genetic Algorithm for feature selection -- A qualitative approximation approach","date":"2024-04-05","arxiv_id":"2404.03996","n_code_links":0,"syntology":null},{"paper":null,"slug":"reliable-feature-selection-for-adversarially","title":"Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection","date":"2024-04-05","arxiv_id":"2404.04188","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-robust-event-guided-low-light-image","title":"Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach","date":"2024-04-01","arxiv_id":"2404.00834","n_code_links":0,"syntology":null},{"paper":null,"slug":"artificial-intelligence-ai-based-prediction","title":"Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19 Patients","date":"2024-03-28","arxiv_id":"2403.19355","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-fair-feature-selection-in-machine","title":"Evaluating Fair Feature Selection in Machine Learning for Healthcare","date":"2024-03-28","arxiv_id":"2403.19165","n_code_links":0,"syntology":null},{"paper":null,"slug":"thelxinoe-recognizing-human-emotions-using","title":"Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning","date":"2024-03-27","arxiv_id":"2403.19014","n_code_links":0,"syntology":null},{"paper":"/paper/predicting-risk-of-cardiovascular-disease","slug":"predicting-risk-of-cardiovascular-disease","title":"Predicting risk of cardiovascular disease using retinal OCT imaging","date":"2024-03-26","arxiv_id":"2403.18873","n_code_links":1,"syntology":null},{"paper":"/paper/integrated-path-stability-selection","slug":"integrated-path-stability-selection","title":"Integrated path stability selection","date":"2024-03-23","arxiv_id":"2403.15877","n_code_links":2,"syntology":null},{"paper":null,"slug":"automated-feature-selection-for-inverse","title":"Automated Feature Selection for Inverse Reinforcement Learning","date":"2024-03-22","arxiv_id":"2403.15079","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-input-auto-encoder-guided-feature","title":"Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems","date":"2024-03-22","arxiv_id":"2403.15511","n_code_links":0,"syntology":null}],"record_sha256":"435b0b26e613a82d510b4f72429b5388e5514b7773c96ccf2f94fb1ee30758be","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}