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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 1–46 of 46 papers

TitleStatusHype
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary OptimizersCode2
Federated Automated Feature Engineering—0
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
Semantic-Guided RL for Interpretable Feature Engineering—0
IIFE: Interaction Information Based Automated Feature EngineeringCode0
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree ReasoningCode1
Learned Feature Importance Scores for Automated Feature Engineering—0
Dynamic and Adaptive Feature Generation with LLM—0
Feature Interaction Aware Automated Data Representation TransformationCode0
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction—0
Feature Programming for Multivariate Time Series PredictionCode1
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Toward Efficient Automated Feature Engineering—0
Feature Selection with Distance Correlation—0
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking AlgorithmsCode1
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Supervised Video Summarization via Multiple Feature Sets with Parallel AttentionCode1
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Machine Learning for Detecting Data Exfiltration: A Review—0
DIFER: Differentiable Automated Feature EngineeringCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
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
Benchmarking Automatic Machine Learning FrameworksCode3
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
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
Automating Feature Engineering—0
ExploreKit: Automatic Feature Generation and SelectionCode0
Cognito: Automated Feature Engineering for Supervised Learning—0
Deep Feature Synthesis: Towards Automating Data Science EndeavorsCode0
Feature Selection as a One-Player Game—0
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