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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 11–20 of 46 papers

TitleStatusHype
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
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
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
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
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