Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory decisions against minority cases. In this work we model such changes in depth and breadth of knowledge as a partitioning of the problem space into regions of differing expertise. We provide here new algorithms that explicitly consider and adapt to the relationship between problem instances and experts' knowledge. We first propose and highlight the drawbacks of a naive approach based on nearest neighbor queries. To address these drawbacks we then introduce a novel algorithm - expertise trees - that constructs decision trees enabling the learner to select appropriate models. We provide theoretical insights and empirically validate the improved performance of our novel approach on a range of problems for which existing methods proved to be inadequate.

翻译：为决策者提供建议的专家，其专业水平往往会随问题实例的不同而变化。在实践中，这可能导致针对少数案例出现次优或歧视性决策。在本研究中，我们将这种知识和广度的变化建模为问题空间按不同专业知识区域的分割。我们提出一系列新算法，能够明确考虑并适应问题实例与专家知识之间的关系。首先，我们提出并揭示了基于最近邻查询的朴素方法的缺陷。为克服这些缺陷，我们随后引入一种全新算法——专业知识树——通过构建决策树使学习器能够选择适当的模型。我们提供了理论见解，并通过实验验证了该方法在现有方法证明不足的多个问题上的性能改进。