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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 26–46 of 46 papers

TitleStatusHype
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research—0
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks—0
Cognito: Automated Feature Engineering for Supervised Learning—0
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification—0
Learned Feature Importance Scores for Automated Feature Engineering—0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Learning Feature Engineering for Classification—0
Techniques for Automated Machine Learning—0
Automating Feature Engineering—0
Machine Learning for Detecting Data Exfiltration: A Review—0
One button machine for automating feature engineering in relational databases—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
Semantic-Guided RL for Interpretable Feature Engineering—0
Toward Efficient Automated Feature Engineering—0
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction—0
Statistical and machine learning ensemble modelling to forecast sea surface temperature—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish—0
Feature Selection as a One-Player Game—0
Feature Selection with Distance Correlation—0
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