{"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/14","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":14,"pages_in_order":30,"rows_per_page":100,"rows":[1301,1400],"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/13","next":"/task/feature-selection/papers/15","papers":[{"url":null,"slug":"automated-deception-detection-from-videos","title":"Automated Deception Detection from Videos: Using End-to-End Learning Based High-Level Features and Classification Approaches","date":"2023-07-13","arxiv_id":"2307.06625","repositories_listed":0,"syntology":null},{"url":null,"slug":"ark-robust-knockoffs-inference-with-coupling","title":"ARK: Robust Knockoffs Inference with Coupling","date":"2023-07-10","arxiv_id":"2307.04400","repositories_listed":0,"syntology":null},{"url":null,"slug":"formulating-a-strategic-plan-based-on","title":"Formulating A Strategic Plan Based On Statistical Analyses And Applications For Financial Companies Through A Real-World Use Case","date":"2023-07-10","arxiv_id":"2307.04778","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-simultaneously-preserving","title":"Feature selection simultaneously preserving both class and cluster structures","date":"2023-07-08","arxiv_id":"2307.03902","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-quantum-regression-algorithm-with","title":"Explainable quantum regression algorithm with encoded data structure","date":"2023-07-07","arxiv_id":"2307.03334","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-word-semantics-and-phonology-affect","title":"How word semantics and phonology affect handwriting of Alzheimer's patients: a machine learning based analysis","date":"2023-07-06","arxiv_id":"2307.04762","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-to-detect-cyber-attacks-and","title":"Machine Learning to detect cyber-attacks and discriminating the types of power system disturbances","date":"2023-07-06","arxiv_id":"2307.03323","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-versatile-hub-model-for-efficient","title":"A Versatile Hub Model For Efficient Information Propagation And Feature Selection","date":"2023-07-05","arxiv_id":"2307.02398","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-emotion-recognition-based-on-galvanic","title":"Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM","date":"2023-07-04","arxiv_id":"2307.05383","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-intrusion-detection-1","title":"Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction","date":"2023-07-04","arxiv_id":"2307.01570","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-a-perspective-on-inter","title":"Feature Selection: A perspective on inter-attribute cooperation","date":"2023-06-28","arxiv_id":"2306.16559","repositories_listed":0,"syntology":null},{"url":null,"slug":"chronic-pain-detection-from-resting-state-raw","title":"Modified Feature Selection for Improved Classification of Resting-State Raw EEG Signals in Chronic Knee Pain","date":"2023-06-27","arxiv_id":"2306.15194","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-and-explanation-of-distributed","title":"Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods","date":"2023-06-27","arxiv_id":"2306.17190","repositories_listed":0,"syntology":null},{"url":"/paper/exploring-dual-model-knowledge-distillation","slug":"exploring-dual-model-knowledge-distillation","title":"Exploring Dual Model Knowledge Distillation for Anomaly Detection","date":"2023-06-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzification-based-feature-selection-for","title":"Fuzzification-based Feature Selection for Enhanced Website Content Encryption","date":"2023-06-23","arxiv_id":"2306.13548","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-chemical-language-a-multimodal","title":"Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction","date":"2023-06-22","arxiv_id":"2306.14919","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-low-rank-update-model-parameter","title":"Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications","date":"2023-06-22","arxiv_id":"2306.12670","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodality-fusion-for-smart-healthcare-a","title":"A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom","date":"2023-06-21","arxiv_id":"2306.11963","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonlinear-feature-aggregation-two-algorithms","title":"Nonlinear Feature Aggregation: Two Algorithms driven by Theory","date":"2023-06-19","arxiv_id":"2306.11143","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-predictive-models-be-used-for-causal","title":"Can predictive models be used for causal inference?","date":"2023-06-18","arxiv_id":"2306.10551","repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-video-quality-assessment-at-the-edge","title":"Blind Video Quality Assessment at the Edge","date":"2023-06-17","arxiv_id":"2306.10386","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-causal-feature-selection","title":"Fair Causal Feature Selection","date":"2023-06-17","arxiv_id":"2306.10336","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzy-feature-selection-with-key-based","title":"Fuzzy Feature Selection with Key-based Cryptographic Transformations","date":"2023-06-16","arxiv_id":"2306.09583","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-feature-selection-and-construction","title":"A Hybrid Feature Selection and Construction Method for Detection of Wind Turbine Generator Heating Faults","date":"2023-06-15","arxiv_id":"2306.09491","repositories_listed":0,"syntology":null},{"url":null,"slug":"imagery-tracking-of-sun-activity-using-2d","title":"Solar Active Regions Detection Via 2D Circular Kernel Time Series Transformation, Entropy and Machine Learning Approach","date":"2023-06-14","arxiv_id":"2306.08270","repositories_listed":0,"syntology":null},{"url":null,"slug":"drcfs-doubly-robust-causal-feature-selection","title":"DRCFS: Doubly Robust Causal Feature Selection","date":"2023-06-12","arxiv_id":"2306.07024","repositories_listed":0,"syntology":null},{"url":null,"slug":"ambulance-demand-prediction-via-convolutional","title":"Ambulance Demand Prediction via Convolutional Neural Networks","date":"2023-06-08","arxiv_id":"2306.04994","repositories_listed":0,"syntology":null},{"url":null,"slug":"sound-explanation-for-trustworthy-machine","title":"Sound Explanation for Trustworthy Machine Learning","date":"2023-06-08","arxiv_id":"2306.06134","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-using-sparse-adaptive","title":"Feature Selection using Sparse Adaptive Bottleneck Centroid-Encoder","date":"2023-06-07","arxiv_id":"2306.04795","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-linear-centroid-encoder-a-convex","title":"Sparse Linear Centroid-Encoder: A Convex Method for Feature Selection","date":"2023-06-07","arxiv_id":"2306.04824","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-tumor-recurrence-vs-radiation-necrosis","title":"Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction","date":"2023-06-05","arxiv_id":"2306.03270","repositories_listed":0,"syntology":null},{"url":null,"slug":"permutation-decision-trees","title":"Permutation Decision Trees","date":"2023-06-05","arxiv_id":"2306.02617","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-private-linear-regression-through","title":"Better Private Linear Regression Through Better Private Feature Selection","date":"2023-06-01","arxiv_id":"2306.00920","repositories_listed":0,"syntology":null},{"url":null,"slug":"distance-rank-score-unsupervised-filter","title":"Distance Rank Score: Unsupervised filter method for feature selection on imbalanced dataset","date":"2023-05-31","arxiv_id":"2305.19804","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-on-sentinel-2-multi","title":"Feature Selection on Sentinel-2 Multi-spectral Imagery for Efficient Tree Cover Estimation","date":"2023-05-31","arxiv_id":"2306.06073","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-identification-of-rotating-machinery","title":"A Graph Reconstruction by Dynamic Signal Coefficient for Fault Classification","date":"2023-05-30","arxiv_id":"2306.05281","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-heart-disease-and-reducing-survey","title":"Predicting Heart Disease and Reducing Survey Time Using Machine Learning Algorithms","date":"2023-05-30","arxiv_id":"2306.00023","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-token-impact-towards-efficient","title":"Predicting Token Impact Towards Efficient Vision Transformer","date":"2023-05-24","arxiv_id":"2305.14840","repositories_listed":0,"syntology":null},{"url":"/paper/joint-feature-and-differentiable-k-nn-graph","slug":"joint-feature-and-differentiable-k-nn-graph","title":"Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy","date":"2023-05-21","arxiv_id":"2305.12396","repositories_listed":0,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 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) · 2 unverified","sample_list":"/paper/joint-feature-and-differentiable-k-nn-graph#ran","syntology_url":"https://syntology.ai/paper/2305.12396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12396"}},"official":null}},{"url":null,"slug":"on-consistency-of-signatures-using-lasso","title":"On Consistency of Signature Using Lasso","date":"2023-05-17","arxiv_id":"2305.10413","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-weighted-random-forests","title":"Optimal Weighted Random Forests","date":"2023-05-17","arxiv_id":"2305.10042","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-separable-multi-dimensional-network-flows","title":"Non-Separable Multi-Dimensional Network Flows for Visual Computing","date":"2023-05-15","arxiv_id":"2305.08628","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-mean-embeddings-and-optimal","title":"Conditional mean embeddings and optimal feature selection via positive definite kernels","date":"2023-05-14","arxiv_id":"2305.08100","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-clustering-algorithms-for","title":"Comparison of Clustering Algorithms for Statistical Features of Vibration Data Sets","date":"2023-05-11","arxiv_id":"2305.06753","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-information-splitting-engineering","title":"Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts","date":"2023-05-10","arxiv_id":"2305.05832","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-febac-a-framework-for-attention","title":"CNN-FEBAC: A framework for attention measurement of autistic individuals","date":"2023-05-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"carbon-price-forecasting-with-quantile","title":"Carbon Price Forecasting with Quantile Regression and Feature Selection","date":"2023-05-05","arxiv_id":"2305.03224","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzy-gene-selection-and-cancer","title":"Fuzzy Gene Selection and Cancer Classification Based on Deep Learning Model","date":"2023-05-04","arxiv_id":"2305.04883","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-feature-engineering-help-quantum-machine","title":"Can Feature Engineering Help Quantum Machine Learning for Malware Detection?","date":"2023-05-03","arxiv_id":"2305.02396","repositories_listed":0,"syntology":null},{"url":null,"slug":"less-vfl-communication-efficient-feature","title":"LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning","date":"2023-05-03","arxiv_id":"2305.02219","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-vision-transformer-layer-choosing","title":"Exploring vision transformer layer choosing for semantic segmentation","date":"2023-05-02","arxiv_id":"2305.01279","repositories_listed":0,"syntology":null},{"url":null,"slug":"misnn-multiple-imputation-via-semi-parametric","title":"MISNN: Multiple Imputation via Semi-parametric Neural Networks","date":"2023-05-02","arxiv_id":"2305.01794","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-machine-learning-for-chd-detection","title":"Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimization","date":"2023-04-30","arxiv_id":"2305.00411","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-multilayer-perceptron-with-feature","title":"Enhanced multilayer perceptron with feature selection and grid search for travel mode choice prediction","date":"2023-04-25","arxiv_id":"2304.12698","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-private-lasso-logistic-regression","title":"Sparse Private LASSO Logistic Regression","date":"2023-04-24","arxiv_id":"2304.12429","repositories_listed":0,"syntology":null},{"url":null,"slug":"sqli-detection-with-ml-a-data-source","title":"SQLi Detection with ML: A data-source perspective","date":"2023-04-24","arxiv_id":"2304.12115","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalised-multi-factor-deep-learning","title":"A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas","date":"2023-04-21","arxiv_id":"2304.10686","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-neural-network-l-0-regularization","title":"Effective Neural Network $L_0$ Regularization With BinMask","date":"2023-04-21","arxiv_id":"2304.11237","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-based-lstm-autoencoder-for","title":"Reconstruction-based LSTM-Autoencoder for Anomaly-based DDoS Attack Detection over Multivariate Time-Series Data","date":"2023-04-21","arxiv_id":"2305.09475","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-image-analysis-with-subspace","title":"Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification","date":"2023-04-19","arxiv_id":"2304.09730","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-detection-of-parkinson-s-disease-using","title":"Early Detection of Parkinson's Disease using Motor Symptoms and Machine Learning","date":"2023-04-18","arxiv_id":"2304.09245","repositories_listed":0,"syntology":null},{"url":null,"slug":"pump-it-up-predict-water-pump-status-using","title":"Pump It Up: Predict Water Pump Status using Attentive Tabular Learning","date":"2023-04-08","arxiv_id":"2304.03969","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-reproducibility-of-machine","title":"Assessing the Reproducibility of Machine-learning-based Biomarker Discovery in Parkinson's Disease","date":"2023-04-06","arxiv_id":"2304.03239","repositories_listed":0,"syntology":null},{"url":null,"slug":"slm-end-to-end-feature-selection-via-sparse","title":"SLM: End-to-end Feature Selection via Sparse Learnable Masks","date":"2023-04-06","arxiv_id":"2304.03202","repositories_listed":0,"syntology":null},{"url":"/paper/synthetic-sample-selection-for-generalized","slug":"synthetic-sample-selection-for-generalized","title":"Synthetic Sample Selection for Generalized Zero-Shot Learning","date":"2023-04-06","arxiv_id":"2304.02846","repositories_listed":0,"syntology":null},{"url":null,"slug":"both-efficiency-and-effectiveness-a-large","title":"Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System","date":"2023-04-05","arxiv_id":"2304.02434","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-good-neural-networks-interpretation","title":"How good Neural Networks interpretation methods really are? A quantitative benchmark","date":"2023-04-05","arxiv_id":"2304.02383","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-as-master-key-unlocking","title":"Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT","date":"2023-04-05","arxiv_id":"2304.02213","repositories_listed":0,"syntology":null},{"url":null,"slug":"opening-the-random-forest-black-box-by-the","title":"Opening the random forest black box by the analysis of the mutual impact of features","date":"2023-04-05","arxiv_id":"2304.02490","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-deep-learning-and-xai-based-algorithm","title":"A New Deep Learning and XAI-Based Algorithm for Features Selection in Genomics","date":"2023-03-29","arxiv_id":"2303.16914","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-heads-are-better-than-one-a-bio-inspired","title":"Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data","date":"2023-03-25","arxiv_id":"2304.06471","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-space-sketching-for-logistic","title":"Feature Space Sketching for Logistic Regression","date":"2023-03-24","arxiv_id":"2303.14284","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-high-performance-blind-image","title":"Lightweight High-Performance Blind Image Quality Assessment","date":"2023-03-23","arxiv_id":"2303.13057","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-reduction-method-comparison-towards","title":"Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems","date":"2023-03-22","arxiv_id":"2303.12891","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fuzzy-adaptive-evolutionary-based-feature","title":"A fuzzy adaptive evolutionary-based feature selection and machine learning framework for single and multi-objective body fat prediction","date":"2023-03-20","arxiv_id":"2303.11949","repositories_listed":0,"syntology":null},{"url":null,"slug":"induced-feature-selection-by-structured","title":"Induced Feature Selection by Structured Pruning","date":"2023-03-20","arxiv_id":"2303.10999","repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-radiomics-and-tumor-biomarkers","title":"Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models","date":"2023-03-20","arxiv_id":"2303.11177","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multimodal-data-driven-framework-for","title":"A Multimodal Data-driven Framework for Anxiety Screening","date":"2023-03-16","arxiv_id":"2303.09041","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-distance-based-approaches-for","title":"Evaluation of distance-based approaches for forensic comparison: Application to hand odor evidence","date":"2023-03-16","arxiv_id":"2303.09126","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-high-dimensional-cyber-physical","title":"Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids","date":"2023-03-15","arxiv_id":"2303.08300","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-exposure-hdr-composition-by-gated-swin","title":"Multi-Exposure HDR Composition by Gated Swin Transformer","date":"2023-03-15","arxiv_id":"2303.08704","repositories_listed":0,"syntology":null},{"url":null,"slug":"egfr-mutation-prediction-using-f18-fdg-pet-ct","title":"EGFR mutation prediction using F18-FDG PET-CT based radiomics features in non-small cell lung cancer","date":"2023-03-14","arxiv_id":"2303.08569","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-eeg-based-emotion-recognition-by","title":"Improving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations","date":"2023-03-14","arxiv_id":"2303.11421","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-quantum-feature-selection","title":"Evolutionary quantum feature selection","date":"2023-03-13","arxiv_id":"2303.07131","repositories_listed":0,"syntology":null},{"url":null,"slug":"solar-power-prediction-using-machine-learning","title":"Solar Power Prediction Using Machine Learning","date":"2023-03-11","arxiv_id":"2303.07875","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lite-fireworks-algorithm-with-fractal","title":"A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection","date":"2023-03-09","arxiv_id":"2303.05516","repositories_listed":0,"syntology":null},{"url":null,"slug":"penalized-deep-partially-linear-cox-models","title":"Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients","date":"2023-03-09","arxiv_id":"2303.05341","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-feature-selection-and","title":"The Impact of Feature Selection and Transformation on Machine Learning Methods in Determining the Credit Scoring","date":"2023-03-09","arxiv_id":"2303.05427","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-together-using-multi-task-learning-to","title":"Better Together: Using Multi-task Learning to Improve Feature Selection within Structural Datasets","date":"2023-03-08","arxiv_id":"2303.04486","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-digital-biomarkers-for-unobtrusive","title":"Extracting Digital Biomarkers for Unobtrusive Stress State Screening from Multimodal Wearable Data","date":"2023-03-08","arxiv_id":"2303.04484","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-sparse-recovery-with-decision-stumps","title":"Optimal Sparse Recovery with Decision Stumps","date":"2023-03-08","arxiv_id":"2303.04301","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-automated-detection-and","title":"A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells","date":"2023-03-07","arxiv_id":"2303.03916","repositories_listed":0,"syntology":null},{"url":null,"slug":"vocalexplore-pay-as-you-go-video-data","title":"VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building [Technical Report]","date":"2023-03-07","arxiv_id":"2303.04068","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-traffic-identification-with-novel","title":"Video traffic identification with novel feature extraction and selection method","date":"2023-03-06","arxiv_id":"2303.03949","repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-feature-selection-techniques","title":"Integration of Feature Selection Techniques using a Sleep Quality Dataset for Comparing Regression Algorithms","date":"2023-03-04","arxiv_id":"2303.02467","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-for-forecasting","title":"Feature Selection with Annealing for Forecasting Financial Time Series","date":"2023-03-03","arxiv_id":"2303.02223","repositories_listed":0,"syntology":null},{"url":null,"slug":"features-disentangled-semantic-broadcast","title":"Features Disentangled Semantic Broadcast Communication Networks","date":"2023-03-03","arxiv_id":"2303.01892","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-extreme-feature-selection-for","title":"Graph-based Extreme Feature Selection for Multi-class Classification Tasks","date":"2023-03-03","arxiv_id":"2303.01792","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-computation-in-action","title":"Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images","date":"2023-03-02","arxiv_id":"2303.00943","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-detection-of-parkinson","title":"Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study","date":"2023-03-02","arxiv_id":"2303.01389","repositories_listed":0,"syntology":null}],"record_sha256":"cfabe01a30f0c006200f82d5f5c64161bcef32f3cdb5e9c654c01a6b1df0dc91","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}