论文标题
自适应协变量获取，以最大程度地减少分类总成本
Adaptive Covariate Acquisition for Minimizing Total Cost of Classification
论文作者
论文摘要
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，为了对患者是否患有糖尿病进行分类，测量葡萄糖耐受性可能很昂贵。假设用户可以指定每个协变量的成本以及错误分类的成本，我们的目标是最大程度地降低（预期的）分类总成本，即错误分类的成本以及所获得的协变量的成本。我们使用（条件）贝叶斯风险将此优化目标正式化，并使用递归程序描述最佳解决方案。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）最佳分类器可以用广义加性模型表示，（2）最佳协变量集限于一系列大小增加的序列。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，所提出的方法在大多数情况下达到的总成本最低。最后，我们通过允许用户指定最低可接受的召回（目标召回）来削弱用户指定所有错误分类费用的要求。我们的实验证实，所提出的方法可以实现目标召回率，同时最大程度地降低了错误的发现率，而协变量的采集成本优于以前的方法。
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.
