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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 21–30 of 46 papers

TitleStatusHype
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
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Machine Learning for Detecting Data Exfiltration: A Review—0
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research—0
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification—0
Lifting Interpretability-Performance Trade-off via Automated Feature EngineeringCode0
Statistical and machine learning ensemble modelling to forecast sea surface temperature—0
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