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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
Solving the "false positives" problem in fraud predictionCode0
The autofeat Python Library for Automated Feature Engineering and SelectionCode0
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMsCode0
Federated Automated Feature Engineering—0
Dynamic and Adaptive Feature Generation with LLM—0
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
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