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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
Feature Programming for Multivariate Time Series PredictionCode1
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking AlgorithmsCode1
Supervised Video Summarization via Multiple Feature Sets with Parallel AttentionCode1
DIFER: Differentiable Automated Feature EngineeringCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
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