{"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/2","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":2,"pages_in_order":17,"rows_per_page":100,"rows":[101,200],"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","next":"/method/feature-selection/papers/3","papers":[{"paper":null,"slug":"quantum-annealing-feature-selection-on-light","title":"Quantum Annealing Feature Selection on Light-weight Medical Image Datasets","date":"2025-02-26","arxiv_id":"2502.19201","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-image-matting-in-real-world-scenes","title":"Enhancing Image Matting in Real-World Scenes with Mask-Guided Iterative Refinement","date":"2025-02-24","arxiv_id":"2502.17093","n_code_links":0,"syntology":null},{"paper":null,"slug":"assessing-a-single-student-s-concentration-on","title":"Assessing a Single Student's Concentration on Learning Platforms: A Machine Learning-Enhanced EEG-Based Framework","date":"2025-02-21","arxiv_id":"2502.15107","n_code_links":0,"syntology":null},{"paper":null,"slug":"ml-driven-approaches-to-combat-medicare-fraud","title":"ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact","date":"2025-02-21","arxiv_id":"2502.15898","n_code_links":0,"syntology":null},{"paper":"/paper/offload-rethinking-by-cloud-assistance-for","slug":"offload-rethinking-by-cloud-assistance-for","title":"Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs","date":"2025-02-21","arxiv_id":"2502.15285","n_code_links":1,"syntology":null},{"paper":null,"slug":"financial-fraud-detection-system-based-on","title":"Financial fraud detection system based on improved random forest and gradient boosting machine (GBM)","date":"2025-02-20","arxiv_id":"2502.15822","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-fetal-birthweight-from-high","title":"Predicting Fetal Birthweight from High Dimensional Data using Advanced Machine Learning","date":"2025-02-20","arxiv_id":"2502.14270","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-assisted-decision-making-with-human","title":"AI-Assisted Decision Making with Human Learning","date":"2025-02-18","arxiv_id":"2502.13062","n_code_links":0,"syntology":null},{"paper":null,"slug":"bolimes-boruta-and-lime-optimized-feature","title":"BOLIMES: Boruta and LIME optiMized fEature Selection for Gene Expression Classification","date":"2025-02-18","arxiv_id":"2502.13080","n_code_links":0,"syntology":null},{"paper":"/paper/sparse-autoencoder-features-for","slug":"sparse-autoencoder-features-for","title":"Sparse Autoencoder Features for Classifications and Transferability","date":"2025-02-17","arxiv_id":"2502.11367","n_code_links":1,"syntology":null},{"paper":"/paper/llm-lasso-a-robust-framework-for-domain","slug":"llm-lasso-a-robust-framework-for-domain","title":"LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization","date":"2025-02-15","arxiv_id":"2502.10648","n_code_links":1,"syntology":null},{"paper":null,"slug":"end-to-end-triplet-loss-based-fine-tuning-for","title":"End-to-End triplet loss based fine-tuning for network embedding in effective PII detection","date":"2025-02-13","arxiv_id":"2502.09002","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-on-data-centric-ai-tabular-learning","title":"A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective","date":"2025-02-12","arxiv_id":"2502.08828","n_code_links":0,"syntology":null},{"paper":null,"slug":"decision-tree-based-wrappers-for-hearing-loss","title":"Decision Tree Based Wrappers for Hearing Loss","date":"2025-02-12","arxiv_id":"2502.08785","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-the-importance-of-features-in","title":"A Study on the Importance of Features in Detecting Advanced Persistent Threats Using Machine Learning","date":"2025-02-11","arxiv_id":"2502.07207","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-scale-feature-fusion-with-image-driven","title":"Multi-Scale Feature Fusion with Image-Driven Spatial Integration for Left Atrium Segmentation from Cardiac MRI Images","date":"2025-02-10","arxiv_id":"2502.06615","n_code_links":0,"syntology":null},{"paper":null,"slug":"contrastive-learning-for-cold-start-1","title":"Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion","date":"2025-02-05","arxiv_id":"2502.03664","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-approach-for-1","title":"Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation","date":"2025-02-05","arxiv_id":"2502.03370","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-driven-student-performance","title":"Machine Learning-Driven Student Performance Prediction for Enhancing Tiered Instruction","date":"2025-02-05","arxiv_id":"2502.03143","n_code_links":0,"syntology":null},{"paper":null,"slug":"policy-guided-causal-state-representation-for","title":"Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation","date":"2025-02-04","arxiv_id":"2502.02327","n_code_links":0,"syntology":null},{"paper":null,"slug":"cohirf-a-scalable-and-interpretable","title":"CoHiRF: A Scalable and Interpretable Clustering Framework for High-Dimensional Data","date":"2025-02-01","arxiv_id":"2502.00380","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-feature-selection-in-causal","title":"Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation","date":"2025-02-01","arxiv_id":"2502.00501","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-strategies-for-parkinson","title":"Machine Learning Strategies for Parkinson Tremor Classification Using Wearable Sensor Data","date":"2025-01-30","arxiv_id":"2501.18671","n_code_links":0,"syntology":null},{"paper":"/paper/saeuron-interpretable-concept-unlearning-in","slug":"saeuron-interpretable-concept-unlearning-in","title":"SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders","date":"2025-01-29","arxiv_id":"2501.18052","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["cywinski/saeuron"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"investigating-application-of-deep-neural","title":"Investigating Application of Deep Neural Networks in Intrusion Detection System Design","date":"2025-01-27","arxiv_id":"2501.15760","n_code_links":0,"syntology":null},{"paper":null,"slug":"multivariate-feature-selection-and","title":"Multivariate Feature Selection and Autoencoder Embeddings of Ovarian Cancer Clinical and Genetic Data","date":"2025-01-27","arxiv_id":"2501.15881","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-statistical-learning-approach-for-feature","title":"A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms","date":"2025-01-26","arxiv_id":"2502.15716","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-and-interpretable-neural-networks","title":"Efficient and Interpretable Neural Networks Using Complex Lehmer Transform","date":"2025-01-25","arxiv_id":"2501.15223","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-efficient-real-time-ddos-detection-model","title":"An Efficient Real Time DDoS Detection Model Using Machine Learning Algorithms","date":"2025-01-24","arxiv_id":"2501.14311","n_code_links":0,"syntology":null},{"paper":null,"slug":"distinguishing-parkinson-s-patients-using","title":"Distinguishing Parkinson's Patients Using Voice-Based Feature Extraction and Classification","date":"2025-01-24","arxiv_id":"2501.14390","n_code_links":0,"syntology":null},{"paper":null,"slug":"permutation-based-multi-objective","title":"Permutation-based multi-objective evolutionary feature selection for high-dimensional data","date":"2025-01-24","arxiv_id":"2501.14310","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhanced-extractor-selector-framework-and","title":"Enhanced Extractor-Selector Framework and Symmetrization Weighted Binary Cross-Entropy for Edge Detections","date":"2025-01-23","arxiv_id":"2501.13365","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-ai-aided-feature-selection-and","title":"Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation","date":"2025-01-23","arxiv_id":"2501.13552","n_code_links":0,"syntology":null},{"paper":null,"slug":"forecasting-of-bitcoin-prices-using-hashrate","title":"Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking Approach","date":"2025-01-22","arxiv_id":"2501.13136","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-dimensional-representation-driven-tsk","title":"Low-Dimensional Representation-Driven TSK Fuzzy System for Feature Selection","date":"2025-01-22","arxiv_id":"2501.12607","n_code_links":0,"syntology":null},{"paper":null,"slug":"frame-forward-recursive-adaptive-model","title":"\"FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection\"","date":"2025-01-21","arxiv_id":"2501.11972","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-baseline-for-machine-learning-based","title":"A baseline for machine-learning-based hepatocellular carcinoma diagnosis using multi-modal clinical data","date":"2025-01-20","arxiv_id":"2501.11535","n_code_links":0,"syntology":null},{"paper":null,"slug":"dlinear-based-prediction-of-remaining-useful","title":"DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence","date":"2025-01-20","arxiv_id":"2501.11542","n_code_links":0,"syntology":null},{"paper":"/paper/finding-reproducible-and-prognostic-radiomic","slug":"finding-reproducible-and-prognostic-radiomic","title":"Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases","date":"2025-01-20","arxiv_id":"2501.11221","n_code_links":1,"syntology":null},{"paper":"/paper/multitask-auxiliary-network-for-perceptual","slug":"multitask-auxiliary-network-for-perceptual","title":"Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images","date":"2025-01-20","arxiv_id":"2501.11512","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-analysis-of-the-combination-of-feature","title":"An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer","date":"2025-01-19","arxiv_id":"2501.10980","n_code_links":0,"syntology":null},{"paper":"/paper/statistical-inference-for-sequential-feature","slug":"statistical-inference-for-sequential-feature","title":"Statistical Inference for Sequential Feature Selection after Domain Adaptation","date":"2025-01-17","arxiv_id":"2501.09933","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-data-centric-ai-a-comprehensive","title":"Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation","date":"2025-01-17","arxiv_id":"2501.10555","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-llm-abilities-to-understand","slug":"evaluating-llm-abilities-to-understand","title":"Evaluating LLM Abilities to Understand Tabular Electronic Health Records: A Comprehensive Study of Patient Data Extraction and Retrieval","date":"2025-01-16","arxiv_id":"2501.09384","n_code_links":1,"syntology":null},{"paper":"/paper/metrics-for-inter-dataset-similarity-with","slug":"metrics-for-inter-dataset-similarity-with","title":"Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version","date":"2025-01-16","arxiv_id":"2501.09591","n_code_links":1,"syntology":null},{"paper":null,"slug":"interpretable-machine-learning-for-predicting","title":"Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system","date":"2025-01-13","arxiv_id":"2501.07247","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-pan-cancer-classification-model-using-multi","title":"A Pan-cancer Classification Model using Multi-view Feature Selection Method and Ensemble Classifier","date":"2025-01-12","arxiv_id":"2501.06805","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-field-visualization-trait-design-and","title":"Multi-field Visualization: Trait design and trait-induced merge trees","date":"2025-01-08","arxiv_id":"2501.06238","n_code_links":0,"syntology":null},{"paper":null,"slug":"pixel-wise-feature-selection-for-perceptual","title":"Pixel-Wise Feature Selection for Perceptual Edge Detection without post-processing","date":"2025-01-05","arxiv_id":"2501.02534","n_code_links":0,"syntology":null},{"paper":null,"slug":"ed-filter-dynamic-feature-filtering-for","title":"ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification","date":"2025-01-04","arxiv_id":"2501.14785","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-differential-diagnosis","title":"Machine Learning-Based Differential Diagnosis of Parkinson's Disease Using Kinematic Feature Extraction and Selection","date":"2025-01-02","arxiv_id":"2501.02014","n_code_links":0,"syntology":null},{"paper":null,"slug":"omnichat-enhancing-spoken-dialogue-systems","title":"OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data for Diverse Scenarios","date":"2025-01-02","arxiv_id":"2501.01384","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-unsupervised-feature-selection-via","title":"Enhancing Unsupervised Feature Selection via Double Sparsity Constrained Optimization","date":"2025-01-01","arxiv_id":"2501.00726","n_code_links":0,"syntology":null},{"paper":null,"slug":"scale-wise-bidirectional-alignment-network","title":"Scale-wise Bidirectional Alignment Network for Referring Remote Sensing Image Segmentation","date":"2025-01-01","arxiv_id":"2501.00851","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-convolution-and-attention-mechanism","title":"A Novel Convolution and Attention Mechanism-based Model for 6D Object Pose Estimation","date":"2024-12-31","arxiv_id":"2501.01993","n_code_links":0,"syntology":null},{"paper":"/paper/scoring-with-large-language-models-a-study-on","slug":"scoring-with-large-language-models-a-study-on","title":"Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues","date":"2024-12-28","arxiv_id":"2412.20264","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-integrated-optimization-and-deep-learning","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","date":"2024-12-27","arxiv_id":"2412.19696","n_code_links":0,"syntology":null},{"paper":null,"slug":"conditional-deep-canonical-time-warping","title":"Conditional Deep Canonical Time Warping","date":"2024-12-24","arxiv_id":"2412.18234","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-forecasting-of-parkinson","title":"Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using MultiLayer Perceptron and LSTM","date":"2024-12-24","arxiv_id":"2412.18248","n_code_links":0,"syntology":null},{"paper":null,"slug":"u-mamba-net-a-highly-efficient-mamba-based-u","title":"U-Mamba-Net: A highly efficient Mamba-based U-net style network for noisy and reverberant speech separation","date":"2024-12-24","arxiv_id":"2412.18217","n_code_links":0,"syntology":null},{"paper":null,"slug":"time-series-feature-redundancy-paradox-an","title":"Time Series Feature Redundancy Paradox: An Empirical Study Based on Mortgage Default Prediction","date":"2024-12-23","arxiv_id":"2501.00034","n_code_links":0,"syntology":null},{"paper":"/paper/bi-sparse-unsupervised-feature-selection","slug":"bi-sparse-unsupervised-feature-selection","title":"Bi-Sparse Unsupervised Feature Selection","date":"2024-12-22","arxiv_id":"2412.16819","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-web-traffic-attacks-identification","title":"Enhancing web traffic attacks identification through ensemble methods and feature selection","date":"2024-12-21","arxiv_id":"2412.16791","n_code_links":0,"syntology":null},{"paper":"/paper/iterative-feature-exclusion-ranking-for-deep","slug":"iterative-feature-exclusion-ranking-for-deep","title":"Iterative Feature Exclusion Ranking for Deep Tabular Learning","date":"2024-12-21","arxiv_id":"2412.16442","n_code_links":1,"syntology":null},{"paper":null,"slug":"assessing-the-impact-of-technical-indicators","title":"Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading","date":"2024-12-19","arxiv_id":"2412.15448","n_code_links":0,"syntology":null},{"paper":null,"slug":"which-imputation-fits-which-feature-selection","title":"Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study","date":"2024-12-18","arxiv_id":"2412.13570","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-phytosensing-ozone-exposure","title":"Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals","date":"2024-12-17","arxiv_id":"2412.13312","n_code_links":0,"syntology":null},{"paper":null,"slug":"pt-a-plain-transformer-is-good-hospital","title":"PT: A Plain Transformer is Good Hospital Readmission Predictor","date":"2024-12-17","arxiv_id":"2412.12909","n_code_links":0,"syntology":null},{"paper":null,"slug":"s-p-500-trend-prediction","title":"S&P 500 Trend Prediction","date":"2024-12-16","arxiv_id":"2412.11462","n_code_links":0,"syntology":null},{"paper":null,"slug":"polymodel-for-hedge-funds-portfolio","title":"PolyModel for Hedge Funds' Portfolio Construction Using Machine Learning","date":"2024-12-15","arxiv_id":"2412.11019","n_code_links":0,"syntology":null},{"paper":"/paper/biological-and-radiological-dictionary-of","slug":"biological-and-radiological-dictionary-of","title":"Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0","date":"2024-12-14","arxiv_id":"2412.10967","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-methodology-in-credit-spread","title":"A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection","date":"2024-12-13","arxiv_id":"2412.09769","n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-selection-for-latent-factor-models","title":"Feature Selection for Latent Factor Models","date":"2024-12-13","arxiv_id":"2412.10128","n_code_links":0,"syntology":null},{"paper":"/paper/one-node-one-model-featuring-the-missing-half","slug":"one-node-one-model-featuring-the-missing-half","title":"One Node One Model: Featuring the Missing-Half for Graph Clustering","date":"2024-12-13","arxiv_id":"2412.09902","n_code_links":1,"syntology":{"ran":4,"of":7,"n_ran_checked":3,"n_instrument":1,"unverified":3,"pointer_only":7,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["xiexuanting/fpgc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-hybrid-framework-for-statistical-feature","title":"A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection","date":"2024-12-11","arxiv_id":"2412.08800","n_code_links":0,"syntology":null},{"paper":null,"slug":"altfs-agency-light-feature-selection-with","title":"AltFS: Agency-light Feature Selection with Large Language Models in Deep Recommender Systems","date":"2024-12-11","arxiv_id":"2412.08516","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multimodal-ensemble-approach-for-clear-cell","title":"A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction","date":"2024-12-10","arxiv_id":"2412.07136","n_code_links":0,"syntology":null},{"paper":"/paper/delay-estimation-based-on-multiple-stage","slug":"delay-estimation-based-on-multiple-stage","title":"Delay Estimation Based on Multiple Stage Message Passing With Attention Mechanism Using a Real Network Communication Dataset","date":"2024-12-10","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"conden-fi-consistency-and-diversity-learning","title":"CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection","date":"2024-12-09","arxiv_id":"2412.06568","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensemble-machine-learning-model-for-inner","title":"Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation","date":"2024-12-09","arxiv_id":"2412.17824","n_code_links":0,"syntology":null},{"paper":null,"slug":"misfeat-feature-selection-for-subgroups-with","title":"MISFEAT: Feature Selection for Subgroups with Systematic Missing Data","date":"2024-12-09","arxiv_id":"2412.06711","n_code_links":0,"syntology":null},{"paper":"/paper/pdg2seq-periodic-dynamic-graph-to-sequence","slug":"pdg2seq-periodic-dynamic-graph-to-sequence","title":"PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction","date":"2024-12-05","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-performance-investigation-of-multimodal","title":"A Performance Investigation of Multimodal Multiobjective Optimization Algorithms in Solving Two Types of Real-World Problems","date":"2024-12-04","arxiv_id":"2412.03013","n_code_links":0,"syntology":null},{"paper":null,"slug":"crash-severity-risk-modeling-strategies-under","title":"Crash Severity Risk Modeling Strategies under Data Imbalance","date":"2024-12-03","arxiv_id":"2412.02094","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-matrix-factorization-with-adaptive","title":"Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering","date":"2024-12-03","arxiv_id":"2412.02292","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimized-iot-intrusion-detection-using","title":"Optimized IoT Intrusion Detection using Machine Learning Technique","date":"2024-12-03","arxiv_id":"2412.02845","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-machine-hearing-system-for-robust-cough","title":"A Machine Hearing System for Robust Cough Detection Based on a High-Level Representation of Band-Specific Audio Features","date":"2024-12-02","arxiv_id":"2412.01996","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-the-use-of-feature-selection-methods","title":"How the use of feature selection methods influences the efficiency and accuracy of complex network simulations","date":"2024-12-02","arxiv_id":"2412.01096","n_code_links":0,"syntology":null},{"paper":null,"slug":"research-on-optimizing-real-time-data","title":"Research on Optimizing Real-Time Data Processing in High-Frequency Trading Algorithms using Machine Learning","date":"2024-12-02","arxiv_id":"2412.01062","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-bare-necessities-designing-simple","title":"The Bare Necessities: Designing Simple, Effective Open-Vocabulary Scene Graphs","date":"2024-12-02","arxiv_id":"2412.01539","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-semantic-communication-for-joint","title":"Generative Semantic Communication for Joint Image Transmission and Segmentation","date":"2024-11-27","arxiv_id":"2411.18005","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-extubation-failure-in-intensive","title":"Predicting Extubation Failure in Intensive Care: The Development of a Novel, End-to-End Actionable and Interpretable Prediction System","date":"2024-11-27","arxiv_id":"2412.00105","n_code_links":0,"syntology":null},{"paper":null,"slug":"causal-inference-in-finance-an-expertise","title":"Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation","date":"2024-11-26","arxiv_id":"2411.17542","n_code_links":0,"syntology":null},{"paper":"/paper/training-a-neural-netwok-for-data-reduction","slug":"training-a-neural-netwok-for-data-reduction","title":"Training a neural netwok for data reduction and better generalization","date":"2024-11-26","arxiv_id":"2411.17180","n_code_links":1,"syntology":null},{"paper":null,"slug":"catnet-effective-fdr-control-in-lstm-with","title":"CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors","date":"2024-11-25","arxiv_id":"2411.16666","n_code_links":0,"syntology":null},{"paper":"/paper/online-high-frequency-trading-stock","slug":"online-high-frequency-trading-stock","title":"Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks","date":"2024-11-23","arxiv_id":"2412.16160","n_code_links":1,"syntology":null},{"paper":null,"slug":"topology-optimization-of-periodic-lattice","title":"Topology optimization of periodic lattice structures for specified mechanical properties using machine learning considering member connectivity","date":"2024-11-21","arxiv_id":"2411.13869","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-evolutional-neural-network-framework-for","title":"An Evolutional Neural Network Framework for Classification of Microarray Data","date":"2024-11-20","arxiv_id":"2411.13326","n_code_links":0,"syntology":null},{"paper":null,"slug":"k-means-derived-unsupervised-feature","title":"K-means Derived Unsupervised Feature Selection using Improved ADMM","date":"2024-11-19","arxiv_id":"2411.15197","n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-selection-for-network-intrusion","title":"Feature Selection for Network Intrusion Detection","date":"2024-11-18","arxiv_id":"2411.11603","n_code_links":0,"syntology":null}],"record_sha256":"0c96acbf45ac2371919407fdaf27451d4c4f20573059b66a43146684fc9ebfe5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}