Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,988편 · 필터 해제
Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers
Credit default prediction is a tabular classification problem in which modest gains in F1 translate directly into reduced financial exposure. We ask whether Instantaneous Quantum Polynomial-time (IQP) circuits can produc…
Feature EngineeringState of Health Estimation using Convolutional and Bidirectional LSTM Neural Networks tuned by Bayesian Optimization
In this research, a novel framework is proposed for the SOH estimation, which employs a hybrid deep learning architecture of a concatenation of a Convolution Neural Network (CNN) and a Bidirectional Long Short-Term Memor…
Feature EngineeringSymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
Tabular data, as a core data format in machine learning, often lacks the discriminative power needed for high-performance modeling due to insufficient feature informativeness. Automated Feature Engineering (AutoFE) overc…
Feature EngineeringFuture Querying: Can LLMs Serve as Implicit Medical World Models?
Traditional clinical prediction models rely on task-specific pipelines and curated, structured data, which scale poorly and underutilize unstructured text. To address this, we introduce future querying, a paradigm that p…
Feature EngineeringGraph Representation Learning of Lightweight IoT Ciphers
SIMON and SIMECK belong to a family of Lightweight Cryptographic Algorithms (LCAs) based on the Feistel block cipher, designed for Internet of Things (IoT) devices. As with all Feistel ciphers, they are susceptible to di…
Graph Representation LearningFeature EngineeringThe impact of feature engineering and an optimisation framework for ocean colour machine learning
Machine learning (ML) is widely used for the development of ocean colour algorithms, but most studies focus on model parameter training and hyperparameter tuning. The optimisation of the data that feeds the models - i.e.…
Feature EngineeringConverting Expert Deliberation into Financial Signals Through A Context-Aware NLP Pipeline
We introduce the CDSP (context-conditional deliberation signal pipeline), converting an investment committee's meeting transcripts into structured predictive features. CDSP segments the meeting transcripts into topical c…
Feature EngineeringCan Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach
Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of their underlying causes at the level of pilot control behavior. Existing explain…
Feature EngineeringFeature ImportanceFew-Shot LearningSIGMA: SHAP-Guided Implicit-Trajectory Generation for Metadata-Free LLM-Based AutoFE
Recent research has leveraged Large Language Models (LLMs) to enhance Automated Feature Engineering (AutoFE) through semantic descriptions and trajectory-based prompting. However, there exist two challenges that limit th…
Feature EngineeringDecoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils
Determining the biological sex of the individuals who created Upper Paleolithic hand stencils remains a challenging problem due to the absence of ground truth, population differences between contemporary and prehistoric …
Feature EngineeringA Multispectral Framework for the Detection of Calcium Carbide-Induced Ripening and Shelf-Life Estimation in Climacteric Fruits
Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of…
Dimensionality ReductionFeature EngineeringTracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Electronic health record (EHR) feature engineering is a major bottleneck in clinical research and AI, accounting for 39-45% of data scientists' workload. This is especially pronounced in heart failure, which affects an e…
Feature EngineeringLM-GRASP: Instance-Specific Language Models for Combinatorial Construction via Online Imitation Learning
Machine learning for combinatorial optimization typically relies on neural constructors trained via reinforcement learning on large offline datasets for a fixed problem class-incurring high pretraining costs and generali…
Reinforcement LearningFeature EngineeringScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and substantial acquisition variation across hospitals. End-to-end imag…
Medical Image ClassificationFeature EngineeringClinical KnowledgeAn Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI
Accurate identification of Alzheimers disease (AD) using resting-state functional magnetic resonance imaging (rs-fMRI) remains challenging due to the high dimensionality, noise, and complex inter-regional dependencies in…
Feature EngineeringTopoFE: topology-aware LLM-guided Automated Feature Engineering
Automatic feature engineering (AutoFE) for tabular learning can be naturally formulated as a program synthesis problem, where the objective is to discover predictive feature transformations from an exponentially large se…
Feature EngineeringProgram SynthesisBayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram
Brain-computer interfaces (BCIs) have long sought calibration-free operation, but classifiers are typically benchmarked by discrimination alone, blind to whether predicted probabilities are well calibrated - a meaningful…
Feature EngineeringAn Exploratory Analysis of Pain Localization via Explainable Computational Modeling
Automatic pain localization, which involves identifying the anatomical origin of pain from peripheral physiological signals without patient self-report, is a clinically critical but largely unaddressed problem, particula…
Feature EngineeringUnderstanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub
Federated Learning (FL) enables collaborative model training without centralizing raw data, but building and operating FL systems remains difficult due to distributed execution, rapidly evolving frameworks, and privacy a…
Feature EngineeringFederated LearningOn the Impact of Entropy-based Features
Network anomaly detection is increasingly challenging due to the growing diversity and variability of traffic patterns, which are not always well captured by traditional statistical features. In this work, we explore the…
Intrusion DetectionFeature EngineeringAnomaly Detection