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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
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
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMsCode0
Techniques for Automated Machine Learning—0
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish—0
The autofeat Python Library for Automated Feature Engineering and SelectionCode0
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks—0
AutoLearn - Automated Feature Generation and SelectionCode0
Solving the "false positives" problem in fraud predictionCode0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
One button machine for automating feature engineering in relational databases—0
Learning Feature Engineering for Classification—0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Cognito: Automated Feature Engineering for Supervised Learning—0
ExploreKit: Automatic Feature Generation and SelectionCode0
Automating Feature Engineering—0
Deep Feature Synthesis: Towards Automating Data Science EndeavorsCode0
Feature Selection as a One-Player Game—0
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