CoFE (Collaborative Feature Engineering) is proposed, a two-phase framework that tightly couples search-based exploration with LLM-driven reasoning and consistently outperforms state-of-the-art data-driven and LLM-based AutoFE methods on the majority of datasets, while offering favorable computational efficiency.
Weihao Jiang, Ziang Nan, Zhihui Shi et al.· Proceedings of the 32nd ACM...· 0 citations
In domains such as finance, healthcare, and industry, feature engineering remains the key bottleneck limiting the performance of machine learning models on tabular data. While Automated Feature Engineering (AutoFE) aims to reduce this manual effort, existing approaches still suffer from distinct limitations: data-drive...
Weihao Jiang, Ziang Nan, Zhihui Shi et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes AutoVerifier, a residual-guided non-parametric optimization method that learns biases from recurring verifier errors and promotes them to code modules or prompt guidance only after replay validation detects no direct regressions, keeping accepted updates auditable, editable, and reusable.
Zelong Zhao, Zhihui Shi, Min-Qi Shi· 0 citations
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