Interpret When Possible:A Tree-Based Hybrid Framework for Interpretable Classification
简介：The demand for explainable artificial intelligence models continues to grow,yet achieving interpretable without compromising the predictive accuracy typical of complex black-box systems remains difficult.While existing hybrid approaches attempt to bridge this gap by combining white-box and black-box models,they often suffer from limitations,including reliance on simpler interpretable components(e.g.,rules and linear models)and the inflexibility of requiring retraining to adjust transparency levels.To address these limitations,we propose a novel Tree-based Hybrid INterpretable(THIN)framework for classification.THIN integrates a compact classification tree with a high-performance black-box model,enabling dynamic control over transparency during inference through a decision-distance-based selection mechanism—without modifying the trained models.A tailored three-stage training algorithm is introduced to construct a high-quality decision tree under the guidance of the black-box model,enhancing its predictive performance while preserving interpretability.Experimental results on a benchmark and large-scale datasets demonstrate that THIN achieves predictive accuracy comparable to state-of-the-art black-box models,while maintaining significantly improved interpretability and lower model complexity compared to traditional decision trees.展开
学者：YifanLIShuhanQiLEICuiChaoXingZhangXuanWang
关键词：interpretable machine learningDecision Trees(DTs)Classification
在线出版日期：2026-05-14 （网站首发日期）
