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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 31–40 of 46 papers

Title	Date	Tasks	Status
Statistical and machine learning ensemble modelling to forecast sea surface temperature	Sep 18, 2019	Automated Feature EngineeringBIG-bench Machine Learning	—Unverified
Techniques for Automated Machine Learning	Jul 21, 2019	Automated Feature EngineeringAutoML	—Unverified
Toward Efficient Automated Feature Engineering	Dec 26, 2022	Automated Feature EngineeringComputational Efficiency	—Unverified
Feature Interaction Aware Automated Data Representation Transformation	Sep 29, 2023	Automated Feature EngineeringDecision Making	CodeCode Available
Catch: Collaborative Feature Set Search for Automated Feature Engineering	Apr 30, 2023	Automated Feature EngineeringDecision Making	CodeCode Available
The autofeat Python Library for Automated Feature Engineering and Selection	Jan 22, 2019	Automated Feature EngineeringFeature Engineering	CodeCode Available
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
ExploreKit: Automatic Feature Generation and Selection	Jan 1, 2016	Automated Feature EngineeringBIG-bench Machine Learning	CodeCode Available
IIFE: Interaction Information Based Automated Feature Engineering	Sep 7, 2024	Automated Feature EngineeringFeature Engineering	CodeCode Available
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning	Dec 23, 2020	Automated Feature EngineeringBIG-bench Machine Learning	CodeCode Available

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