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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

Title	Date	Tasks	Status	Score
AutoLearn - Automated Feature Generation and Selection	Nov 17, 2017	Automated Feature EngineeringFeature Engineering	CodeCode Available	5
Deep Feature Synthesis: Towards Automating Data Science Endeavors	Jan 1, 2015	Automated Feature Engineering	CodeCode Available	5
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning	Dec 23, 2020	Automated Feature EngineeringBIG-bench Machine Learning	CodeCode Available	5
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science	Mar 20, 2016	Automated Feature EngineeringAutoML	CodeCode Available	5
Catch: Collaborative Feature Set Search for Automated Feature Engineering	Apr 30, 2023	Automated Feature EngineeringDecision Making	CodeCode Available	5
Lifting Interpretability-Performance Trade-off via Automated Feature Engineering	Feb 11, 2020	Automated Feature EngineeringFeature Engineering	CodeCode Available	5
Feature Interaction Aware Automated Data Representation Transformation	Sep 29, 2023	Automated Feature EngineeringDecision Making	CodeCode Available	5
ExploreKit: Automatic Feature Generation and Selection	Jan 1, 2016	Automated Feature EngineeringBIG-bench Machine Learning	CodeCode Available	5
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI Researchers	Oct 22, 2024	Automated Feature EngineeringAutoML	CodeCode Available	5
IIFE: Interaction Information Based Automated Feature Engineering	Sep 7, 2024	Automated Feature EngineeringFeature Engineering	CodeCode Available	5

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