Inductive learning methods for critical decision making applications

用于关键决策应用的归纳学习方法

• 批准号: 250377-2007
• 负责人: Yao, JingTao
• 金额: $ 1.02万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=363059
• 关键词: Inductive; learning; methods; critical; decision

Machine learning is an important research area in computer science. The key activity of machine learning is to extract rules from data sets that can be used to predict results for unseen data accurately. In other words, we try to develop algorithms and techniques that allow computers to learn from data. In this proposal, I will study the theoretical aspects as well as the techniques of granular computing, Self-Organizing Feature Maps (SOFM) and Support Vector Machines (SVM), and apply them to critical decision making problems, such as intrusion detection, marketing analysis, financial prediction, and cancer diagnosis.     As an emerging technique, granular computing has attracted many researchers. The goal of this research is to enhance the learning ability of conventional algorithms as well as to provide more general and flexible granulation algorithms for classification problems and decision making applications.     In SOFM, data are clustered based on a set of neurons with the ability to distinguish themselves in terms of differences between input vectors. The competitive process of determining which neuron best fits a particular input vector is challenging when there are many similarity measures are taken into consideration. Game theory and other techniques will be explored to enhance the competitive process in SOFM and thus give it the ability to better organize and analyze more data characteristics.     SVM has demonstrated excellent performance in real-world applications such as categorization, recognition, and classification. We have adapted rough set theory to SVM for feature selection problems and successfully applied it to an intrusion detection problem. I will continue to explore possible improvements in SVM models for this problem.     Our ultimate goal is to develop a decision support system that will help users determine which learning method is most suitable for different applications.

机器学习是计算机科学的一个重要研究领域。机器学习的关键活动是从数据集中提取规则，这些规则可用于准确预测未知数据的结果。换句话说，我们试图开发算法和技术，让计算机从数据中学习。在这个提案中，我将研究理论方面以及粒度计算，自组织特征映射（SOFM）和支持向量机（SVM）的技术，并将其应用于关键决策问题，如入侵检测，营销分析，财务预测和癌症诊断。 粒计算作为一种新兴的计算技术，吸引了众多的研究者。本研究的目标是提高传统算法的学习能力，以及提供更通用和灵活的粒度分类问题和决策应用程序的算法。 在SOFM中，数据基于一组神经元进行聚类，这些神经元能够根据输入向量之间的差异来区分自己。当考虑到许多相似性度量时，确定哪个神经元最适合特定输入向量的竞争过程具有挑战性。博弈论和其他技术将被探索，以加强竞争过程中的SOFM，从而使其能够更好地组织和分析更多的数据特征。 SVM在分类、识别和分类等实际应用中表现出了优异的性能。我们将粗糙集理论应用于支持向量机来解决特征选择问题，并成功地将其应用于入侵检测问题。我将继续探索SVM模型对这个问题的可能改进。 我们的最终目标是开发一个决策支持系统，帮助用户确定哪种学习方法最适合不同的应用程序。