Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,990편 · 필터 해제
The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Data
This study investigates the predictive capacity of environmental, temporal, and spatial factors on traffic accident severity in the United States. Using a dataset of 500,000 U.S. traffic accidents spanning 2016-2023, we …
Feature Engineeringseverity predictionFeature ImportanceOptimized Hybrid Feature Engineering for Resource-Efficient Arrhythmia Detection in ECG Signals: An Optimization Framework
Cardiovascular diseases, particularly arrhythmias, remain a leading global cause of mortality, necessitating continuous monitoring via the Internet of Medical Things (IoMT). However, state-of-the-art deep learning approa…
Arrhythmia DetectionFeature EngineeringAutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt
Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management. However, due to significant privacy concerns surrounding traffic…
Personalized Federated LearningFeature EngineeringTraffic PredictionClinical Document Metadata Extraction: A Scoping Review
Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast d…
Feature EngineeringPhysics-Informed Lightweight Machine Learning for Aviation Visibility Nowcasting Across Multiple Climatic Regimes
Short-term prediction (nowcasting) of low-visibility and precipitation events is critical for aviation safety and operational efficiency. Current operational approaches rely on computationally intensive numerical weather…
Feature EngineeringPhishing Detection System: An Ensemble Approach Using Character-Level CNN and Feature Engineering
In actuality, phishing attacks remain one of the most prevalent cybersecurity risks in existence today, with malevolent actors constantly changing their strategies to successfully trick users. This paper presents an AI m…
Feature EngineeringThe Semantic Architect: How FEAML Bridges Structured Data and LLMs for Multi-Label Tasks
Existing feature engineering methods based on large language models (LLMs) have not yet been applied to multi-label learning tasks. They lack the ability to model complex label dependencies and are not specifically adapt…
Multi-Label ClassificationMulti-Label LearningFeature EngineeringCode GenerationSign-Aware Multistate Jaccard Kernels and Geometry for Real and Complex-Valued Signals
We introduce a sign-aware, multistate Jaccard/Tanimoto framework that extends overlap-based distances from nonnegative vectors and measures to arbitrary real- and complex-valued signals while retaining bounded metric and…
Feature EngineeringUnderstanding Structured Financial Data with LLMs: A Case Study on Fraud Detection
Detecting fraud in financial transactions typically relies on tabular models that demand heavy feature engineering to handle high-dimensional data and offer limited interpretability, making it difficult for humans to und…
Feature EngineeringFraud DetectionA Novel Patch-Based TDA Approach for Computed Tomography Imaging
The development of machine learning models based on computed tomography (CT) imaging has been a major focus due to the promise that imaging holds for diagnosis, staging, and prognostication. These models often rely on th…
Feature EngineeringSigTime: Learning and Visually Explaining Time Series Signatures
Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signa…
Feature EngineeringBoosted Random Forests for Predicting Treatment Failure of Chemotherapy Regimens
Cancer patients may undergo lengthy and painful chemotherapy treatments, comprising several successive regimens or plans. Treatment inefficacy and other adverse events can lead to discontinuation (or failure) of these pl…
Feature EngineeringSequence-to-Image Transformation for Sequence Classification Using Rips Complex Construction and Chaos Game Representation
Traditional feature engineering approaches for molecular sequence classification suffer from sparsity issues and computational complexity, while deep learning models often underperform on tabular biological data. This pa…
Feature EngineeringSPROCKET: Extending ROCKET to Distance-Based Time-Series Transformations With Prototypes
Classical Time Series Classification algorithms are dominated by feature engineering strategies. One of the most prominent of these transforms is ROCKET, which achieves strong performance through random kernel features. …
Time Series ClassificationFeature EngineeringCan TabPFN Compete with GNNs for Node Classification via Graph Tabularization?
Foundation models pretrained on large data have demonstrated remarkable zero-shot generalization capabilities across domains. Building on the success of TabPFN for tabular data and its recent extension to time series, we…
Zero-shot GeneralizationFeature EngineeringNode ClassificationLASER: Language Model Regression for Semi-Structured Workflow Resource and Runtime Estimation
Accurate prediction of resource consumption and runtime for cloud workflow jobs is critical for scheduling efficiency, yet remains challenging due to the semi-structured nature of job configurations -- comprising shell c…
Feature EngineeringFinancial Fraud Identification and Interpretability Study for Listed Companies Based on Convolutional Neural Network
Since the emergence of joint-stock companies, financial fraud by listed firms has repeatedly undermined capital markets. Fraud is difficult to detect because of covert tactics and the high labor and time costs of audits.…
Feature EngineeringFraud DetectionHierarchical geometric deep learning enables scalable analysis of molecular dynamics
Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established quantitative descriptors (features). Gra…
Feature EngineeringEnhancing Dimensionality Prediction in Hybrid Metal Halides via Feature Engineering and Class-Imbalance Mitigation
We present a machine learning framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering…
Feature EngineeringA Prescriptive Framework for Determining Optimal Days for Short-Term Traffic Counts
The Federal Highway Administration (FHWA) mandates that state Departments of Transportation (DOTs) collect reliable Annual Average Daily Traffic (AADT) data. However, many U.S. DOTs struggle to obtain accurate AADT, espe…
Feature Engineering