Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,990편 · 필터 해제
SemPiper: Interactive Code Synthesis for Semantic Operators in Machine Learning Pipelines
Machine learning (ML) pipelines require extensive data preparation, feature engineering, and integration across heterogeneous sources, making them tedious and error-prone to develop. While large language models (LLMs) ha…
Feature EngineeringTime-Series Foundation Model Embeddings for Remaining Useful Life Estimation
Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled datasets to train task-specific sequenc…
Feature EngineeringLeveraging Social Media Data for COVID-19 Studies
Nowadays, social media networks have become widely preferred sources of information. Especially during the time of the Coronavirus disease 2019 COVID 19 pandemic, social media has been one of the most used platforms to g…
Feature EngineeringLATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)
Feature engineering remains a cornerstone of tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for its automation, giving rise to LLM-powered Automated Tabular Feature Engineeri…
Feature EngineeringPrompt EngineeringBridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practitioners inspect the features driving model decisions, and models must …
Feature EngineeringHybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018
Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems…
Feature EngineeringIntrusion DetectionMultimodal Sexism Identification and Characterization using Large Language Models and Gradient Boosting
We present the AILS-NTUA submission to the EXIST 2026 Lab at CLEF, addressing multimodal sexism identification and characterization in memes (Task 2) and short-form videos (Task 3). Our system follows a feature-engineere…
Feature EngineeringA Hierarchical Feature Engineering Framework for Automated Classification of Phonotraumatic and Non-Phonotraumatic Vocal Hyperfunction
Ambulatory neck-surface acceleration enables non-invasive monitoring of vocal hyperfunction, yet robust biomarkers for its subtypes remain limited. This study investigates the NeckVibe Challenge dataset to distinguish ph…
Feature EngineeringSurvPFN: Towards Foundation Models for Survival Predictions
Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched. Unlike in standard regression tasks, survi…
Feature EngineeringTabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remains a critical yet underexplored component of real-world modeling pipe…
Feature EngineeringDiffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
Latent defect screening is challenged by extremely low failure rates, high-dimensional test data, and absence of labeled anomalies. We propose the first unsupervised anomaly detection framework incorporating a Diffusion …
Unsupervised Anomaly DetectionFeature EngineeringATWL: A Formal Language for Representing, Comparing, and Reusing Visual Analytics Workflows
Visual analytics (VA) workflows are inherently complex, involving data transformation, feature engineering, visual representation, and human interpretation. They are typically described in unstructured prose, hindering s…
Feature EngineeringLLM Agent Based Renewable Energy Forecasting Using Edge and IoT Data A Review of Solar Wind Weather and Grid Aware Decision Support
Reliable forecasting of renewable energy generation is a foundational requirement for grid stability energy trading battery scheduling and carbon aware operational planning Solar and wind resources are inherently intermi…
Feature EngineeringEureka: Intelligent Feature Engineering for Enterprise AI Cloud Resource Demand Prediction
Effective features are crucial for predictive model performance, but creating them often requires domain expertise, limiting scalability across applications. We define feature engineering as an agentic code generation pr…
Reinforcement LearningFeature EngineeringCode GenerationGEESE: Genotype-aware End-to-End Spatio-temporal Embedding for Behavioral Phenotyping
Behavioral phenotyping of genetic animal models currently requires labor-intensive manual feature engineering that limits reproducibility and scalability. We present GEESE, an end-to-end deep learning framework that lear…
Feature EngineeringAssessing the Operational Viability of Foundation Models for Time Series Forecasting
Time series forecasting drives operational decisions in areas like finance, transportation, and energy. While supervised learning approaches achieve strong performance, they require domain-specific training, feature engi…
Time Series ForecastingFeature EngineeringEvolutionary Multi-Task Optimization for LLM-Guided Program Discovery
Recent LLM-guided evolutionary search methods have shown that iterative program mutation can discover strong algorithms, but they typically optimize each task independently, even when related tasks share reusable structu…
Feature EngineeringMapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis
Field-scale crop maps support supply-chain forecasting and policy, yet statewide crop identification still often depends on retrospective surveys or remote-sensing workflows built around hand-engineered spectral features…
Feature EngineeringResponse-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning
Response-free item difficulty modelling promises to reduce reliance on response-based calibration but is intrinsically difficult on reading-comprehension multiple-choice items, where difficulty depends on inferential dem…
Feature EngineeringMulti-Task LearningQuestion AnsweringTowards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
Relational databases store much of the world's structured information, and they are essential for driving complex predictive applications. However, deep learning progress on relational data remains limited, as convention…
Feature Engineering