最小化分类总代价的自适应协变量获取
Adaptive Covariate Acquisition for Minimizing Total Cost of Classification
摘要：
在某些应用中，获取协变量的成本不可忽略。例如，在医疗领域中，为了对患者是否患有糖尿病进行分类，测量葡萄糖耐量可能是昂贵的。假设每个协变量的成本和误分类的成本可以由用户指定，我们的目标是最小化（预期）总分类成本，即误分类成本加上所获取的协变量的成本。我们正式这个优化目标使用（条件）贝叶斯风险和描述的最佳解决方案，使用递归过程。由于该过程在计算上是不可行的，因此我们引入了两个假设：（1）最佳分类器可以由广义可加模型表示，（2）最佳协变量集被限制为一个序列的集合的大小增加。我们表明，在这两个假设下，存在一个计算有效的解决方案。此外，在几个医疗数据集上，我们表明，与以前的各种方法相比，该方法在大多数情况下实现了最低的总成本。最后，我们削弱了对用户的要求，指定所有的错误分类成本，允许用户指定最低限度的可接受的召回（目标召回）。我们的实验证实，该方法实现了目标召回率，同时最大限度地减少了错误发现率和协变量获取成本优于以往的方法。
英文摘要：
In some applications, acquiring covariates comes at a cost which is not negligible. For example in the medical domain, in order to classify whether a patient has diabetes or not, measuring glucose tolerance can be expensive. Assuming that the cost of each covariate, and the cost of misclassification can be specified by the user, our goal is to minimize the (expected) total cost of classification, i.e. the cost of misclassification plus the cost of the acquired covariates. We formalize this optimization goal using the (conditional) Bayes risk and describe the optimal solution using a recursive procedure. Since the procedure is computationally infeasible, we consequently introduce two assumptions: (1) the optimal classifier can be represented by a generalized additive model, (2) the optimal sets of covariates are limited to a sequence of sets of increasing size. We show that under these two assumptions, a computationally efficient solution exists. Furthermore, on several medical datasets, we show that the proposed method achieves in most situations the lowest total costs when compared to various previous methods. Finally, we weaken the requirement on the user to specify all misclassification costs by allowing the user to specify the minimally acceptable recall (target recall). Our experiments confirm that the proposed method achieves the target recall while minimizing the false discovery rate and the covariate acquisition costs better than previous methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
