论文标题
一项关于缓解基于决策树的不确定性估计的硬性界限的研究
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models
论文作者
论文摘要
数据驱动的AI模型的结果不能始终是正确的。为了估计这些结果的不确定性，已经提出了不确定性包装框架，该框架考虑了与模型拟合，输入质量和范围合规性相关的不确定性。不确定性包装器使用决策树方法来群集输入质量相关的不确定性，将输入严格分配给不同的不确定性群集。因此，仅一项特征的略有变化可能会导致群集分配的不确定性明显不同。我们的目标是通过一种方法来替代此任务的艰苦决策边界，同时保留可解释性，运行时复杂性和预测性能。选择了五种方法作为候选人，并将其集成到不确定性包装框架中。对于基于Brier评分的评估，使用Carla Simulator和Yolov3生成了行人检测用例的数据集。所有综合方法都达到了不确定性估计的软化，即平滑。但是，与决策树相比，它们并不容易解释，并且运行时的复杂性更高。此外，Brier分数的某些组成部分受损，而另一些则改善了。关于布里尔评分的最有希望的是随机森林。总之，软化艰难的决策树边界似乎是一个权衡决策。
Outcomes of data-driven AI models cannot be assumed to be always correct. To estimate the uncertainty in these outcomes, the uncertainty wrapper framework has been proposed, which considers uncertainties related to model fit, input quality, and scope compliance. Uncertainty wrappers use a decision tree approach to cluster input quality related uncertainties, assigning inputs strictly to distinct uncertainty clusters. Hence, a slight variation in only one feature may lead to a cluster assignment with a significantly different uncertainty. Our objective is to replace this with an approach that mitigates hard decision boundaries of these assignments while preserving interpretability, runtime complexity, and prediction performance. Five approaches were selected as candidates and integrated into the uncertainty wrapper framework. For the evaluation based on the Brier score, datasets for a pedestrian detection use case were generated using the CARLA simulator and YOLOv3. All integrated approaches achieved a softening, i.e., smoothing, of uncertainty estimation. Yet, compared to decision trees, they are not so easy to interpret and have higher runtime complexity. Moreover, some components of the Brier score impaired while others improved. Most promising regarding the Brier score were random forests. In conclusion, softening hard decision tree boundaries appears to be a trade-off decision.
