Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,990편 · 필터 해제
Data-efficient Bayesian-guided design selection from large candidate sets: Application to hyperelastic stochastic metamaterials
From a pool of admissible designs, we aim to identify a structure that achieves a target macroscopic stress response. For each candidate, the response is obtained from a high-fidelity oracle, such as expensive computatio…
Feature EngineeringActive LearningArgumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us
Computational argumentation offers formal frameworks for transparent, verifiable reasoning but has traditionally been limited by its reliance on domain-specific information and extensive feature engineering. In contrast,…
Feature EngineeringLeJOT-AutoML: LLM-Driven Feature Engineering for Job Execution Time Prediction in Databricks Cost Optimization
Databricks job orchestration systems (e.g., LeJOT) reduce cloud costs by selecting low-priced compute configurations while meeting latency and dependency constraints. Accurate execution-time prediction under heterogeneou…
Feature EngineeringSynthetic-Child: An AIGC-Based Synthetic Data Pipeline for Privacy-Preserving Child Posture Estimation
Accurate child posture estimation is critical for AI-powered study companion devices, yet collecting large-scale annotated datasets of children is both expensive and ethically prohibitive due to privacy concerns. We pres…
Feature EngineeringTrinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users
Early-stage users in a new scenario intensify cold-start challenges, yet prior works often address only parts of the problem through model architecture. Launching a new user experience to replace an established product i…
Feature EngineeringExploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection
Cross-domain intrusion detection remains a critical challenge due to significant variability in network traffic characteristics and feature distributions across environments. This study evaluates the transferability of t…
Feature EngineeringIntrusion DetectionFeature ImportancePredicting Tennis Serve directions with Machine Learning
Serves, especially first serves, are very important in professional tennis. Servers choose their serve directions strategically to maximize their winning chances while trying to be unpredictable. On the other hand, retur…
Feature EngineeringRelatron: Automating Relational Machine Learning over Relational Databases
Predictive modeling over relational databases (RDBs) powers applications, yet remains challenging due to capturing both cross-table dependencies and complex feature interactions. Relational Deep Learning (RDL) methods au…
Feature EngineeringKMLP: A Scalable Hybrid Architecture for Web-Scale Tabular Data Modeling
Predictive modeling on web-scale tabular data with billions of instances and hundreds of heterogeneous numerical features faces significant scalability challenges. These features exhibit anisotropy, heavy-tailed distribu…
Feature EngineeringAn Explainable Ensemble Framework for Alzheimer's Disease Prediction Using Structured Clinical and Cognitive Data
Early and accurate detection of Alzheimer's disease (AD) remains a major challenge in medical diagnosis due to its subtle onset and progressive nature. This research introduces an explainable ensemble learning Framework …
Feature EngineeringFeature ImportanceEnsemble LearningMedical DiagnosisHuman-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark
Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide. We investigate how human guida…
Feature EngineeringComparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation
Accurate soil moisture (SM) estimation is critical for precision agriculture, water resources management and climate monitoring. Yet, existing satellite SM products are too coarse (>1km) for farm-level applications. We p…
Feature EngineeringExplaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
AutoClustering methods aim to automate unsupervised learning tasks, including algorithm selection (AS), hyperparameter optimization (HPO), and pipeline synthesis (PS), by often leveraging meta-learning over dataset meta-…
Hyperparameter OptimizationFeature EngineeringFeature ImportanceFAMOSE: A ReAct Approach to Automated Feature Discovery
Feature engineering remains a critical yet challenging bottleneck in machine learning, particularly for tabular data, as identifying optimal features from an exponentially large feature space traditionally demands substa…
Feature EngineeringAxle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring
Reliable and cost-effective maintenance is essential for railway safety, particularly at the wheel-rail interface, which is prone to wear and failure. Predictive maintenance frameworks increasingly leverage sensor-genera…
Feature EngineeringContinual LearningAnomaly DetectionCausally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning
Automated feature engineering (AFE) enables AI systems to autonomously construct high-utility representations from raw tabular data. However, existing AFE methods rely on statistical heuristics, yielding brittle features…
Multi-agent Reinforcement LearningFeature EngineeringPredicting The Cop Number Using Machine Learning
Cops and Robbers is a pursuit evasion game played on a graph, first introduced independently by Quilliot \cite{quilliot1978jeux} and Nowakowski and Winkler \cite{NOWAKOWSKI1983235} over four decades ago. A main interest …
Feature EngineeringCoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization
Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottleneck for traditional methods, which operate within rigid search spaces and lack domain awareness. While Large Language Models…
Hyperparameter OptimizationFeature EngineeringMedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction
In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasingly used to automate this process, acting as domain experts that propose diver…
Feature EngineeringFeature ImportanceAttention-Based Deep Learning for Early Parkinson's Disease Detection with Tabular Biomedical Data
Early and accurate detection of Parkinson's disease (PD) remains a critical challenge in medical diagnostics due to the subtlety of early-stage symptoms and the complex, non-linear relationships inherent in biomedical da…
Feature Engineering