KnowFeat: Knowledge-Guided Feature Engineering via LLM Agents

2026-09-03

AI Summary

为解决特征工程中缺乏领域知识、验证和可解释性问题，提出KnowFeat框架，通过组织五类领域知识并结合LLM代理生成特征，并经三阶段验证确保其有效性。

Abstract

Automated feature engineering with large language models (LLMs) can produce semantically meaningful features for tabular data, yet existing methods lack structured domain knowledge, rigorous verification, and explainable provenance. We propose KnowFeat, a knowledge-guided feature engineering framework that organizes domain knowledge into five types -- schema metadata, regulatory indicators, detection rules, expert opinions, and court document evidence -- and injects them as structured context into an LLM agent. A three-stage verification pipeline filters candidates through code execution, statistical quality checks, and model effectiveness evaluation. Every accepted feature carries a provenance record tracing its design to specific knowledge assets. Under a strict held-out protocol that eliminates feature-selection leakage, KnowFeat ranks first (avg. rank 2.3) across twelve public benchmarks among seven methods (one-sided Wilcoxon p=0.017), with a peak gain of +11.6 pp AUC on a telecom churn dataset. On a real-world Bitcoin anti-money laundering (AML) dataset (Elliptic) and a synthetic digital currency AML benchmark (SimECNY), KnowFeat maintains competitive detection performance with full provenance traceability.

Problem

• Automated feature engineering
• large language models
• domain knowledge
• verification
• explainable provenance

Innovation

• Knowledge-Guided
• Feature Engineering
• LLM Agents
• Verification Pipeline
• Provenance Traceability