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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
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
Benchmarking Automatic Machine Learning FrameworksCode3
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary OptimizersCode2
Supervised Video Summarization via Multiple Feature Sets with Parallel AttentionCode1
Feature Programming for Multivariate Time Series PredictionCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree ReasoningCode1
DIFER: Differentiable Automated Feature EngineeringCode1
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking AlgorithmsCode1
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
AutoLearn - Automated Feature Generation and SelectionCode0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
Deep Feature Synthesis: Towards Automating Data Science EndeavorsCode0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
ExploreKit: Automatic Feature Generation and SelectionCode0
Feature Interaction Aware Automated Data Representation TransformationCode0
IIFE: Interaction Information Based Automated Feature EngineeringCode0
Lifting Interpretability-Performance Trade-off via Automated Feature EngineeringCode0
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
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Learning Feature Engineering for Classification—0
Techniques for Automated Machine Learning—0
Automating Feature Engineering—0
Machine Learning for Detecting Data Exfiltration: A Review—0
One button machine for automating feature engineering in relational databases—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
Semantic-Guided RL for Interpretable Feature Engineering—0
Toward Efficient Automated Feature Engineering—0
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction—0
Statistical and machine learning ensemble modelling to forecast sea surface temperature—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
Feature Selection as a One-Player Game—0
Feature Selection with Distance Correlation—0
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