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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
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Learning Feature Engineering for Classification—0
Machine Learning for Detecting Data Exfiltration: A Review—0
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
Semantic-Guided RL for Interpretable Feature Engineering—0
Statistical and machine learning ensemble modelling to forecast sea surface temperature—0
Techniques for Automated Machine Learning—0
Toward Efficient Automated Feature Engineering—0
Feature Interaction Aware Automated Data Representation TransformationCode0
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
The autofeat Python Library for Automated Feature Engineering and SelectionCode0
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
ExploreKit: Automatic Feature Generation and SelectionCode0
IIFE: Interaction Information Based Automated Feature EngineeringCode0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Deep Feature Synthesis: Towards Automating Data Science EndeavorsCode0
AutoLearn - Automated Feature Generation and SelectionCode0
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMsCode0
Solving the "false positives" problem in fraud predictionCode0
Lifting Interpretability-Performance Trade-off via Automated Feature EngineeringCode0
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