Multiclass classification under prioritized error control and specific error costs with applications to dementia classification

项目摘要 全球有5000万人患有痴呆症，每年新增近1000万例。这个 痴呆症的亚型包括阿尔茨海默病(AD)、血管性痴呆、帕金森氏病(PD)、 路易体痴呆，以及一组导致额颞部痴呆的疾病。早早地和 对痴呆症病因的准确诊断至关重要，因为它可以导致及时提供症状 治疗和避免服用可能加重症状并有助于发展和评估的药物 新的药物和获得有效治疗的机会。因此，有了 广泛研究开发准确的分类器(例如，线性判别分析、支持向量 机器、多分类Logistic回归、随机森林、增强、卷积神经网络)到 自动将痴呆症分类为不同的类别。在大多数现有的工作中，分类器被设计成 最大限度地提高总体准确性。由于不同类型分类错误可能具有不同的后果 (成本)，开发具有优先差错控制的多类分类器是非常理想的。有一个很有钱的 关于将假阴性率降至最低的二进制分类器的文献，其中假阳性率控制在 特定的级别。一个突出的例子是Neyman-Pearson分类框架。然而， 将框架扩展到多类别分类，并结合所需的优先差错控制， 虽然至关重要，但在很大程度上仍不为人所知。 此项目通过开发带有控件的多类分类通用框架来填补这一空白 错误分类错误，同时为另一组错误分类错误类型施加各种(相对)成本。 这可以被视为对成本敏感的学习和Neyman-Pearson分类在 多类设置。新方法的发展将优化多类别的临床诊断 通过更有效的临床试验设置和加速药物发现。我们的具体目标是： 目标1.提出一个灵活的框架，该框架包括区分优先顺序的差错控制要求以及 各种分类错误类型，并开发了一种高效的伞形算法来解决相关的 约束优化问题。 目的2.研究该优化问题的可行性和伞形算法的性质。 目的3.通过广泛的模拟研究对所提出的算法进行评估，并将其应用于痴呆亚型 分类，并开发一个公开可用的R包。

Project Summary Fifty million people worldwide have dementia, and there are nearly 10 million new cases every year. The subtypes of dementia include Alzheimer's disease (AD), vascular dementia, Parkinson's disease (PD), dementia with Lewy bodies, and a group of diseases that contribute to frontotemporal dementia. Early and accurate diagnosis of the dementia cause is crucial because it can lead to the timely provision of symptomatic treatment and avoidance of medications that may worsen symptoms and assist in developing and evaluating new drugs and gaining access to effective treatments when they become available. There has thus been extensive research on developing accurate classifiers (e.g., linear discriminant analysis, support vector machines, multiclass logistic regression, random forests, boosting, convolutional neural network) to automatically classify dementia into different classes. In most existing work, the classifiers are designed to maximize overall accuracy. Since different types of classification error may have different consequences (costs), it is highly desirable to develop multiclass classifiers with prioritized error control. There is a rich literature on binary classifiers that minimize the false negative rate, with false positive rate controlled under a particular level. A prominent example is the Neyman-Pearson classification framework. However, the extension of the framework to multiclass classification, in conjunction with the desired prioritized error controls, while of vital importance, remains largely unknown. This project fills this gap by developing a general framework for multiclass classification with controls for misclassification errors, while imposing various (relative) costs for another set of misclassification error types. This can be viewed as a unification of cost-sensitive learning and the Neyman-Pearson classification in the multiclass setting. The new methodological development will optimize clinical diagnosis in the multiclass setting and expedite drug discovery through more efficient clinical trials. Our specific aims are: Aim 1. To propose a flexible framework that includes prioritized error control requirements as well as costs for various classification error types, and develop an efficient umbrella algorithm to solve the associated constrained optimization problem. Aim 2. To study the feasibility of the optimization problem and the properties of the umbrella algorithm. Aim 3. To evaluate the proposed algorithm via extensive simulation studies, apply it to dementia subtype classification, and develop a publicly available R package.