Statistical Learning With Expert Knowledge and Complex Data

理解数据是我们这个世纪面临的最大挑战之一。十年前，许多应用程序中的主要问题是获取相关数据并正确存储，以便用于统计建模，推理和预测。如今，收集各种数据变得更加容易。然而，从这些数据中创建可解释的知识，开发新的学习算法和构建准确的预测模型正在成为更具挑战性的任务。 目前的统计学习方法通过使用和/或开发特定的学习模型（或一个委员会）来解决这些问题。研究人员通常可以获得特定领域专家的初步评估，早期调查的结果，或与感兴趣的问题相关的廉价测量。一个有效的策略是首先检查从基础人群中随机选择的少量样本集（有时非常大），以便为研究确定更具代表性的（基于等级的）数据，或者为已经观察到的数据分配等级。额外的等级信息可以与通常的学习方法协同工作，以设计更有效的学习策略，并在学习算法和专家知识之间创建交互式学习和谐。 直接应用现有的方法在文献中的排名为基础的数据强制严格的假设，往往是不适当的。我提出的研究计划的重点是开发新的方法，学习算法和计算工具，以积极地将基于排名的数据的排名信息嵌入到学习过程中，并为以数据为中心的问题产生高度准确的统计学习技术。基于排名的预测模型，深度神经网络，支持向量机和有限混合模型将被开发用于有效的预测，分类和聚类目的。 开发的方法将在其实施计算密集型，并涉及复杂的建模。对这些模型进行严格的数学分析，将有助于深入了解从基于等级的数据进行推断的过程，以及等级信息的价值，以及如何使用这些信息来构建更可靠和高度准确的估计和预测模型。所开发的技术将用于真实的应用中，例如医学研究中的诊断研究（例如，使用图像数据和放射科医师的初步评估的骨质疏松症诊断）和/或工业（例如，高压绝缘子中的局部放电分类）。 基于等级的统计学习技术的发展将为加拿大的技术转让和创收机会带来很大的潜力。该研究的10名HQP学员将接受高级多学科培训，以满足数据科学领域对多学科加拿大专家日益增长的需求。

Making sense of data is one of the great challenges of our century. A decade ago, the major issue in many applications was to obtain relevant data and properly store it to be used in statistical modeling, inference and prediction. Today, it is much easier to collect all kinds of data. However, creating interpretable knowledge from this data, developing new learning algorithms and constructing accurate predictive models are becoming more challenging tasks. Current approaches to statistical learning provide solutions to these problems by using and/or developing specific learning models (or a committee of them) that work with the data in hand. Often researchers have access to primary assessments of area specific experts, results of earlier surveys, or inexpensive measurements related to the problem of interest. An efficient strategy is to first examine a small number of randomly selected sets of samples from the underlying population (sometimes very massive) to either identify more representative (rank-based) data for the study or assign ranks to already observed data. The extra rank information can work in tandem with the usual learning methods to design more effective learning strategies and create an interactive learning harmony between the learning algorithms and expert knowledge. Straightforward applications of available methods in the literature on rank-based data force stringent assumptions that are not often appropriate. My proposed research program is focused on the development of new methodologies, learning algorithms and computational tools to actively imbed the rank information of rank-based data into the learning process and produce highly accurate statistical learning techniques for data centric problems. Rank-based predictive models, deep neural networks, support vector machines and finite mixture models will be developed for efficient prediction, classification, and clustering purposes. The developed methods will be computationally intensive in their implementation and involve complex modeling. Rigorous mathematical analysis of such models will provide insight into the process of inference from rank-based data, as well as the value of the rank information and how this information may be used to construct more reliable and highly accurate estimation and prediction models. The developed techniques will be used in real applications such as diagnosis studies in medical research (e.g., osteoporosis diagnosis using image data and radiologists' primary assessments) and/or industry (e.g., partial discharge classification in high voltage insulators). The development of rank-based statistical learning techniques will have a high degree of potential for technology transfer and opportunities for revenue generation for Canada. The team of 10 HQP trainees of this research will have advanced multidisciplinary training to meet the growing need for multidisciplinary Canadian experts in the area of Data Science.