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
The metrics of individual modality contribution (IMC) and multimodal synergistic gain (MSG) are introduced to quantify sample-level and semantic-level utility, so as to guide semantic denoising selection and robust conditional balancing strategies, effectively mitigating noise interference.
Yan Zhang, Xiaoye Miao, Yanming Yu et al.· Proceedings of the 32nd ACM...· 0 citations
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