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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
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
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