AI RESEARCH
LLM-Driven Reasoning for Constraint-Aware Feature Selection in Industrial Systems
arXiv CS.CL
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ArXi:2603.24979v1 Announce Type: new Feature selection is a crucial step in large-scale industrial machine learning systems, directly affecting model accuracy, efficiency, and maintainability. Traditional feature selection methods rely on labeled data and statistical heuristics, making them difficult to apply in production environments where labeled data are limited and multiple operational constraints must be satisfied.