FELA deploys specialized LLM agents in an evolutionary framework to generate, validate, and refine explainable features from heterogeneous industrial event logs, improving downstream model performance.
Large language models can automatically engineer features for few-shot tabular learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 2verdicts
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LLM-FE is a framework that treats feature engineering as LLM-driven program search with data feedback, reporting consistent gains over baselines on classification and regression tabular tasks.
citing papers explorer
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FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Data
FELA deploys specialized LLM agents in an evolutionary framework to generate, validate, and refine explainable features from heterogeneous industrial event logs, improving downstream model performance.
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LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
LLM-FE is a framework that treats feature engineering as LLM-driven program search with data feedback, reporting consistent gains over baselines on classification and regression tabular tasks.