CAREER: Large-Scale Multi-label Learning

职业：大规模多标签学习

• 批准号: 0643494
• 负责人: Rong Jin
• 金额: $ 49.32万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-15 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0643494&HistoricalAwards=false
• 关键词: CAREER; Large; Scale; Multi; label

Important applications in science and business depend on automatic classification of large volumes of items (examples) into predefined classes. Multi-label learning refers to the classification problem where each example can be assigned to multiple class labels simultaneously. The classification problem is germane to many different domains, such as natural language processing, computer vision, human computer interaction, bioinformatics, health care, and physiology. Existing machine learning technologies are unsuitable for large-scale multi-label learning because they are unable to handle rare class classification problems and poorly distinguish classes with similar input patterns. To overcome the limitation of the existing approaches, the project is developing a relation propagation framework for multi-label learning that explicitly exploits the similarity of examples and the correlation among classes simultaneously. In particular, this project includes research to (1) develop efficient optimization algorithms for the proposed relation propagation framework; (2) develop effective algorithms for learning the similarity of examples and the correlation among classes; (3) develop effective active learning algorithms for multi-label learning; and (4) evaluate the proposed framework for multi-label learning through three real world applications: text categorization, image annotation, and prediction of gene expression patterns.The project will advance the state of the art of techniques for large-scale multi-label learning through the development of relation propagation framework, which in return will have a significant impact on a wide range of applications. The research results will also enhance the current machine learning curricula, involve students in interdisciplinary research and improve the education of the information technology workforce. The project Web site (http://www.cse.msu.edu/~rongjin/) will be used for research results dissemination.

科学和商业中的重要应用依赖于将大量项目（示例）自动分类为预定义的类别。多标签学习是指每个样本可以同时分配给多个类别标签的分类问题。分类问题与许多不同的领域有密切关系，例如自然语言处理，计算机视觉，人机交互，生物信息学，医疗保健和生理学。现有的机器学习技术不适合大规模的多标签学习，因为它们无法处理罕见的类别分类问题，并且难以区分具有相似输入模式的类别。为了克服现有方法的局限性，该项目正在开发一个用于多标签学习的关系传播框架，该框架同时显式地利用示例的相似性和类之间的相关性。具体而言，该项目包括以下研究：（1）为所提出的关系传播框架开发有效的优化算法;（2）为学习示例的相似性和类间的相关性开发有效的算法;（3）为多标签学习开发有效的主动学习算法;以及（4）通过三个真实的应用程序评估所提出的多标签学习框架：文本分类、图像注释和基因表达模式预测。该项目将通过开发关系传播框架，推进大规模多标签学习技术的最新发展，从而对广泛的应用产生重大影响。研究成果还将加强目前的机器学习课程，让学生参与跨学科研究，并改善信息技术劳动力的教育。该项目的网站（http：//www.cse.msu.edu/cnrongjin/）将用于传播研究成果。