SEI: Knowledge-Based Data Classification, Approximation and Optimization

SEI：基于知识的数据分类、近似和优化

• 批准号: 0511905
• 负责人: Olvi Mangasarian
• 金额: $ 49.6万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0511905&HistoricalAwards=false
• 关键词: SEI; Knowledge; Based; Data; Classification

AbstractNSF-0511905Mangasarian, OlviMassive datasets occur in all types of settings ranging from the highly scientific to the ubiquitous internet. Making sense of this massive data requires sophisticated computer sciences techniques such as data classification, approximation and optimization. All of these techniques can be improved substantially by making effective use of prior knowledge that is often readily available. For example doctors' experience can be utilized in obtaining improved classifiers for various types of important problems, such as medical diagnosis and prognosis. Since the most powerful state-of the-art classifiers are based on support vector machines, which in turn are formulated as constrained or unconstrained optimization problems, the aim is that prior knowledge be incorporated into various optimization-based applications such as classification and approximation problems as well into the theory of optimization itself. To a large degree, this proposal is motivated by the investigators' extensive collaborative work with oncologists, surgeons and medical physicists and the investigators' desire to make full use of the expertise of such practitioners by incorporating it into computable but rigorous models.The intellectual merit of the proposed work lies in the use of rigorous theory and problem analysis techniques that incorporate domain specific information into general optimization problems. The research will first incorporate knowledge into a linear or nonlinear support vector machine classifier and show that such incorporation is possible by appending additional constraints to the original problem. Preliminary tests indicate improvements in classifier accuracy. Secondly, prior knowledge will be introduced into approximation problems. Thus, in addition to given discrete data that is normally used to generate an approximation to an unknown function, prior knowledge is also taken into account. Finally, prior knowledge will be incorporated into general constrained or unconstrained optimization problems, wherein the prior knowledge consists of new constraints to be imposed on the behavior of the objective function on various regions. The generality of these new techniques will facilitate the integration of information from disparate sources, since the theory allows multiple sets of prior information to be included concurrently. Specific application to radiotherapy treatment planning problems will ensure the computer science advancements are demonstrably useful in a particular problem domain.The optimization, modeling, and computational techniques will provide a boost to advances in cancer diagnosis and prognosis, chemotherapy, and other treatment regimes. The knowledge-based approach encompasses a broad spectrum of important classification and approximation problems that have wide applicability in science and engineering. The work will also raise the profile of data mining techniques in other areas such as surgery, pharmacology, and medical research, by demonstrating how the methodologies can be utilized to incorporate prior knowledge into both planning and design issues, and improving both efficiency of delivery and effectiveness of treatment in many clinical settings. By coupling the education of several computer science and engineering students with the proposed work, a new group of multidisciplinary researchers will be trained that will ensure the technical advances are applied to further application domains.

AbstractNSF-0511905Mangasarian，Olvi 海量数据集出现在从高度科学到无处不在的互联网的所有类型的环境中。理解这些海量数据需要复杂的计算机科学技术，例如数据分类、近似和优化。通过有效利用通常容易获得的先验知识，所有这些技术都可以得到显着改进。例如，可以利用医生的经验来获得针对各种类型的重要问题（例如医学诊断和预后）的改进的分类器。由于最强大的最先进的分类器基于支持向量机，而支持向量机又被表述为受约束或无约束的优化问题，因此目标是将先验知识纳入各种基于优化的应用（例如分类和近似问题）以及优化理论本身。 在很大程度上，这一提议的动机是研究人员与肿瘤学家、外科医生和医学物理学家的广泛合作，以及研究人员希望通过将这些从业者的专业知识纳入可计算但严格的模型中来充分利用这些专业知识。该提议工作的智力价值在于使用严格的理论和问题分析技术，将领域特定信息纳入一般优化问题。 该研究将首先将知识整合到线性或非线性支持向量机分类器中，并表明通过向原始问题附加附加约束可以实现这种整合。 初步测试表明分类器准确性有所提高。 其次，将先验知识引入到近似问题中。 因此，除了通常用于生成未知函数的近似值的给定离散数据之外，还考虑了先验知识。 最后，先验知识将被纳入一般的约束或无约束优化问题中，其中先验知识包括对目标函数在各个区域的行为施加的新约束。 这些新技术的通用性将有助于整合来自不同来源的信息，因为该理论允许同时包含多组先验信息。放疗治疗计划问题的具体应用将确保计算机科学的进步在特定问题领域明显有用。优化、建模和计算技术将推动癌症诊断和预后、化疗和其他治疗方案的进步。 基于知识的方法涵盖了广泛的重要分类和近似问题，在科学和工程中具有广泛的适用性。 这项工作还将通过展示如何利用这些方法将先验知识纳入规划和设计问题，并提高许多临床环境中的治疗效率和治疗效果，提高数据挖掘技术在外科、药理学和医学研究等其他领域的知名度。 通过将几名计算机科学和工程专业学生的教育与拟议的工作结合起来，将培训一批新的多学科研究人员，以确保技术进步应用于进一步的应用领域。