Abstract In classification, an algorithm learns to classify a given instance based on a set of observed attribute values. In many real world cases testing the value of an attribute incurs a cost. Furthermore, there can also be a cost associated with the misclassification of an instance. Cost sensitive classification attempts to minimize the expected cost of classification, by deciding after each observed attribute value, which attribute to measure next. In this paper we suggest Partially Observable Markov Decision Processes (POMDPs) as a modeling tool for cost sensitive classification. POMDPs are typically solved through a policy over belief states. We show how a relatively small set of potentially important belief states can be identified, and define an MDP over these belief states. To identify these potentially important belief states, we construct standard decision trees over all attribute subsets, and the leaves of these trees become the state space of our tree-based MDP. At each phase we decide on the next attribute to measure, balancing the cost of the measurement and the classification accuracy. We compare our approach to a set of previous approaches, showing our approach to work better for a range of misclassification costs.

中文翻译：

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使用 POMDP 学习成本敏感的决策树

摘要 在分类中，算法学习根据一组观察到的属性值对给定实例进行分类。在许多现实世界的案例中，测试属性的值会产生成本。此外，还可能存在与实例错误分类相关的成本。成本敏感分类试图通过在每个观察到的属性值之后决定下一个要测量的属性来最小化分类的预期成本。在本文中，我们建议将部分可观察马尔可夫决策过程 (POMDP) 作为成本敏感分类的建模工具。POMDP 通常通过对信念状态的策略来解决。我们展示了如何识别一组相对较小的潜在重要信念状态，并定义这些信念状态的 MDP。为了识别这些潜在的重要信念状态，我们在所有属性子集上构建标准决策树，这些树的叶子成为我们基于树的 MDP 的状态空间。在每个阶段，我们决定下一个要测量的属性，平衡测量成本和分类准确度。我们将我们的方法与一组以前的方法进行比较，展示了我们的方法可以更好地应对一系列错误分类成本。