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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
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks—0
Federated Automated Feature Engineering—0
Cognito: Automated Feature Engineering for Supervised Learning—0
Dynamic and Adaptive Feature Generation with LLM—0
Automating Feature Engineering—0
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research—0
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction—0
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification—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
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