发表机构
IMT Mines Alès（阿莱斯矿业学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出实用引导程序，衡量用户对集合值分类器可接受的不精确程度，通过迭代方法在表格和图像数据集上验证其收敛性和实用性。
AI 中文摘要
集合值分类器，无论是从精确概率和适应性成本函数推导而来，还是从具有鲁棒推理机制的凸集推导而来，或是从共形方法推导而来，都是获得更鲁棒、更可信预测的常规选项。然而，缺乏操作性工具来衡量给定用户在接收预测时准备接受多大的鲁棒性或不精确性，即他/她准备放弃多少精度以换取更高的准确性。因此，在本文中，我们提出了实用且可操作的引导程序，以衡量用户对集合值预测的倾向性。迭代引导程序在收敛到目标参数值方面的有效性在来自标准机器学习基准的表格和图像数据集上得到了证明。结果表明，该程序还向用户呈现少量实例，突显了该方法在现实世界应用中的实用性，这些应用旨在识别决策者在面对不精确性时的最优行为。
英文摘要
Set-valued classifiers, whether derived from precise probabilities and an adapted cost function, from convex sets with a robust inference mechanism, or from conformal methods, are routine options to obtain more robust, trustworthy predictions. However, there is a lack of operational tools to measure how robust or imprecise a given user is ready to be when receiving predictions, that is how much precision he/she is ready to let go in exchange of more accuracy. This is why we propose, in this paper, practical and operational elicitation procedures to measure the user proneness to set-valued predictions. The effectiveness of the iterative elicitation procedure in converging to the target parameter value is demonstrated on both tabular and image datasets drawn from standard machine learning benchmarks. The results show that the procedure also presents the user with a small number of instances, highlighting the practicality of the approach for real-world applications aimed at identifying the decision maker's optimal behavior when faced with imprecision.
