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Automated Feature Engineering

Automated feature engineering improves upon the traditional approach to feature engineering by automatically extracting useful and meaningful features from a set of related data tables with a framework that can be applied to any problem.

Papers

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Showing 1–25 of 46 papers

TitleStatusHype
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary OptimizersCode2
Federated Automated Feature Engineering—0
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
Semantic-Guided RL for Interpretable Feature Engineering—0
IIFE: Interaction Information Based Automated Feature EngineeringCode0
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree ReasoningCode1
Learned Feature Importance Scores for Automated Feature Engineering—0
Dynamic and Adaptive Feature Generation with LLM—0
Feature Interaction Aware Automated Data Representation TransformationCode0
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction—0
Feature Programming for Multivariate Time Series PredictionCode1
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Toward Efficient Automated Feature Engineering—0
Feature Selection with Distance Correlation—0
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking AlgorithmsCode1
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Supervised Video Summarization via Multiple Feature Sets with Parallel AttentionCode1
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Machine Learning for Detecting Data Exfiltration: A Review—0
DIFER: Differentiable Automated Feature EngineeringCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research—0
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