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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 1–10 of 46 papers

TitleStatusHype
Benchmarking Automatic Machine Learning FrameworksCode3
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary OptimizersCode2
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree ReasoningCode1
DIFER: Differentiable Automated Feature EngineeringCode1
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Feature Programming for Multivariate Time Series PredictionCode1
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking AlgorithmsCode1
Supervised Video Summarization via Multiple Feature Sets with Parallel AttentionCode1
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