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光华讲坛—A unified performance measurement framework for classification algorithms 一种针对分类算法的统一性能度量框架
发布时间: 2023-10-08

主题:A unified performance measurement framework for classification algorithms    


主讲人:美国特拉华大学 陈滨桐教授

主持人:5822yh银河国际・(中国)官方网站 李伟教授



主办单位:5822yh银河国际・(中国)官方网站 科研处


Bintong Chen graduated from Shanghai Jiaotong University with dual B.S. degrees in ship-building/naval architecture and electrical engineering. He received M.S. in systems engineering and Ph.D. in operations management/research from the Wharton School, the University of Pennsylvania. He is currently a professor of the Lerner College of Business and Economics and the director of the Institute for Financial Services Analytics at University of Delaware. He published many high quality papers in the area of optimization theory, data-driven analytics, and business applications. He received many outstanding research and teaching awards in institutions he worked. Professor Chen consulted many international companies, including JP Morgan Chase, Agriculture Bank of China, AT&T, Burlington Northern Rail, Delaware Department of Transportation, Nordstrom, and AstraZeneca, etc. He was a board member for APICS, the largest supply chain professional association in North American.

陈滨桐,特拉华大学勒纳商学与经济学院教授,于1985年获上海交通大学船舶结构与海洋工程系、电子工程系双学士学位,1987年获宾夕法尼亚大学工程与应用科学学院系统工程系硕士学位,1990年获宾夕法尼亚大学沃顿商学院运筹与信息管理系博士学位。现为特拉华大学勒纳商学与经济学院金融服务分析中心主任、博士项目主任。陈滨桐教授在管理科学、运筹学和运营管理领域取得了丰硕的研究成果,有多项研究成果发表在相关领域全球顶级学术期刊,包括《Management Science》和《Operations Research》,曾在诸多国际期刊编辑委员会中任职,包括《POM》和《Omega》期刊,并在所工作的大学中获得了许多杰出的研究和教学奖项。陈教授曾为许多国际公司提供咨询服务,包括摩根大通、中国农业银行、美国电话电报公司、伯灵顿北方铁路、特拉华州交通部、诺德斯特龙和阿斯利康等。他曾是北美最大的供应链专业协会——美国生产与库存管理协会(APICS)的董事会成员。


Many classification algorithm performance measures have been independently proposed and studied. Two questions arise about these measurements: (1) When do they measure the maximum potential of a classification algorithm? (2) How to efficiently identify and calculate the maximum performance for each measurement? We propose a unified theoretical framework that includes all existing performance measures and curves as special cases. To answer the first question, we investigate two variable transformations and apply theoretical findings to various measures and performance curves. To answer the second question, we classify all performance measures into three categories: monotone measures, unimodal measures, and multi-modal measures, based on the process to search for the optimal threshold. The unified framework allows us to systematically analyze the properties of classification algorithm performance measures and provides guidance to design new performance measures.