Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,990편 · 필터 해제
Automated Vigilance State Classification in Rodents Using Machine Learning and Feature Engineering
Preclinical sleep research remains constrained by labor intensive, manual vigilance state classification and inter rater variability, limiting throughput and reproducibility. This study presents an automated framework de…
Feature EngineeringUncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods
Brain tumor resection is a highly complex procedure with profound implications for survival and quality of life. Predicting patient outcomes is crucial to guide clinicians in balancing oncological control with preservati…
Predicting Patient OutcomesFeature EngineeringAn Efficient Deep Learning Framework for Brain Stroke Diagnosis Using Computed Tomography Images
Brain stroke is a leading cause of mortality and long-term disability worldwide, underscoring the need for precise and rapid prediction techniques. Computed Tomography (CT) scan is considered one of the most effective me…
Stroke ClassificationFeature EngineeringAdvancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction
Accurately predicting magnetic behavior across diverse materials systems remains a longstanding challenge due to the complex interplay of structural and electronic factors and is pivotal for the accelerated discovery and…
Feature EngineeringFormation EnergyDiscovery of Fatigue Strength Models via Feature Engineering and automated eXplainable Machine Learning applied to the welded Transverse Stiffener
This research introduces a unified approach combining Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to predict fatigue strength in welded transverse stiffener details. It integrates e…
Feature EngineeringFeature ImportancePrompt Mechanisms in Medical Imaging: A Comprehensive Survey
Deep learning offers transformative potential in medical imaging, yet its clinical adoption is frequently hampered by challenges such as data scarcity, distribution shifts, and the need for robust task generalization. Pr…
Feature EngineeringImage GenerationPrompt EngineeringSurveyQuantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market
We propose a hybrid quantum-classical reinforcement learning framework for sector rotation in the Taiwan stock market. Our system employs Proximal Policy Optimization (PPO) as the backbone algorithm and integrates both c…
Automated Feature EngineeringFeature Engineeringreinforcement-learningReinforcement LearningTemporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection
Cryptocurrency transaction fraud detection faces the dual challenges of increasingly complex transaction patterns and severe class imbalance. Traditional methods rely on manual feature engineering and struggle to capture…
Anomaly DetectionFeature EngineeringFraud DetectionGraph Anomaly Detection+1Tabular Feature Discovery With Reasoning Type Exploration
Feature engineering for tabular data remains a critical yet challenging step in machine learning. Recently, large language models (LLMs) have been used to automatically generate new features by leveraging their vast know…
Feature EngineeringA Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners
Rock bolts are crucial components of the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent…
Feature EngineeringSemantic SegmentationRelational Deep Learning: Challenges, Foundations and Next-Generation Architectures
Graph machine learning has led to a significant increase in the capabilities of models that learn on arbitrary graph-structured data and has been applied to molecules, social networks, recommendation systems, and transpo…
Deep LearningFeature EngineeringRecommendation SystemsEnhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation
Accurate time series forecasting models are often compromised by data drift, where underlying data distributions change over time, leading to significant declines in prediction performance. To address this challenge, thi…
Drift DetectionFeature EngineeringTime SeriesTime Series ForecastingAdvanced fraud detection using machine learning models: enhancing financial transaction security
The rise of digital payments has accelerated the need for intelligent and scalable systems to detect fraud. This research presents an end-to-end, feature-rich machine learning framework for detecting credit card transact…
Feature EngineeringFraud DetectionOptimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition
This study conducts an empirical examination of MLP networks investigated through a rigorous methodical experimentation process involving three diverse datasets: TinyFace, Heart Disease, and Iris. Study Overview: The stu…
Dimensionality ReductionFeature Engineeringfeature selectionFeature Engineering for Agents: An Adaptive Cognitive Architecture for Interpretable ML Monitoring
Monitoring Machine Learning (ML) models in production environments is crucial, yet traditional approaches often yield verbose, low-interpretability outputs that hinder effective decision-making. We propose a cognitive ar…
Feature EngineeringThe Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Machine learning has promised to change the landscape of laboratory chemistry, with impressive results in molecular property prediction and reaction retro-synthesis. However, chemical datasets are often inaccessible to t…
Active LearningBenchmarkingFeature EngineeringMolecular Property Prediction+3Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning
Forecasting violent conflict at high spatial and temporal resolution remains a central challenge for both researchers and policymakers. This study presents a novel neural network architecture for forecasting three distin…
Feature EngineeringHumanitarianExploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer
Cryptocurrency price dynamics are driven largely by microstructural supply demand imbalances in the limit order book (LOB), yet the highly noisy nature of LOB data complicates the signal extraction process. Prior researc…
Feature EngineeringTransformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Short-term sentiment forecasting in financial markets (e.g., stocks, indices) is challenging due to volatility, non-linearity, and noise in OHLC (Open, High, Low, Close) data. This paper introduces a novel CMG (Chaos-Mar…
Feature EngineeringGaussian ProcessesUniversal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
Recommender systems are pivotal in delivering personalized experiences across industries, yet their adoption and scalability remain hindered by the need for extensive dataset- and task-specific configurations. Existing s…
Feature EngineeringModel SelectionRecommendation Systems