主题：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
主讲人：美国特拉华大学 陈滨桐教授
主持人：成功彩票 李伟教授
时间：2023年10月11日（周三）14:30-16:30
举办地点：颐德楼H308
主办单位：成功彩票 科研处
主讲人简介：
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。
内容简介:
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.
许多分类算法的性能度量方法都是被独立提出和研究。关于这些度量方法存在两个问题：（1）它们在何时度量分类算法的最大潜力？（2）如何高效识别并计算每个度量方法的最大性能？我们提出了一个统一的理论框架，其中包括所有现有的性能度量方法和曲线作为特例。为回答第一个问题，我们研究了两个变量变换，并将理论发现应用到了各种度量方法和性能曲线上。为回答第二个问题，我们基于搜索最优阈值的过程将所有性能度量方法分为了三类：单调度量、单峰度量和多峰度量。这个统一的框架使我们能够系统地分析分类算法性能度量的属性，并为设计新的性能度量方法提供指导。
