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Zhihuan Peng

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Open access 2026

MCTS-Plan: A History-Aware Adaptive Search Framework for Hierarchical AutoML in Multi-Label Classification

Automated machine learning for multi-label classification, referred to as multi-label AutoML, is challenging because a candidate pipeline must jointly select label-transformation strategies, multi-label learners, single-label learners, feature engineering operators, preprocessing operators, and their conditional hyperp...

Zhi-Huan Peng, Peng Qin, Tao Zhang et al. · IEEE Access · 0 citations

Open access Jul 2026

LLM-MetaAS: A Semantic-Statistical Policy Routing Framework for AutoML Execution Strategy Selection

LLM-MetaAS is proposed, a semantic-statistical framework for AutoML execution policy selection that improves overall AutoML performance and selects policies closer to the oracle than fixed strategies, random selection, and the native Auto-sklearn 2.0 selector.

Zhihuan Peng, Pincheng Liu, Yong Li et al. · Applied Sciences · 0 citations