发表机构
Fujitsu Limited（富士通公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对决策树更新时树结构解释变化的问题，提出BCLTX损失指标与改进CART的CART-BCTX算法，经10个真实数据集验证，其预测性能与BCLTX值权衡良好，计算时间与CART相当。
AI 中文摘要
在机器学习模型的运行过程中，模型更新是一个基础流程，需要仔细考虑其对下游决策的影响。尤其是在运行可解释模型时，模型更新导致的解释变化可能会给用户带来不利结果。决策树由于透明度高，常被用于风险敏感型决策，也是上述问题较为突出的典型案例。然而，现有解决类似问题的研究聚焦于基于特征贡献的解释，无法处理源自树结构的解释。因此，本文提出了基于树的解释中的向后兼容性损失（BCLTX），这是一种抑制模型更新前后决策树解释变化的损失指标。此外，我们设计了基于树的解释中向后兼容性的CART算法（CART-BCTX），这是一种针对BCLTX下决策树更新问题对CART进行改进的轻量算法。使用包含分类和回归任务的10个真实世界数据集开展的实验结果表明，无论任务类型如何，CART-BCTX在预测性能与BCLTX值之间取得了良好的权衡，且计算时间与CART相当。
英文摘要
In the operation of machine learning models, model update is a fundamental process that requires careful consideration of its impact on downstream decision-making. Particularly when operating explainable models, changes in explanations resulting from model updates can lead to detrimental outcomes for users. Decision trees, due to their high transparency, are frequently employed in risk-sensitive decision-making and serve as a prominent example in which the aforementioned issue is evident. However, existing research addressing similar issues has focused on explanations based on feature contributions, and thus cannot handle explanations derived from tree structures. Therefore, this paper proposes the Backward Compatibility Loss in Tree-based eXplanations (BCLTX), a loss metric that suppresses changes in decision tree explanations before and after updates. Furthermore, we design CART with Backward Compatibility in Tree-based eXplanations (CART-BCTX), a lightweight algorithm that improves upon CART for the decision tree update problem under BCLTX. Experimental results using 10 real-world datasets, including both classification and regression tasks, show that CART-BCTX achieves favorable trade-offs between prediction performances and BCLTX values, with comparable computation times to CART, regardless of the task.
