讲座题目：A unified performance measurement framework for classification algorithms
讲座时间：2023年9月23日（周六）上午9：00
讲座地点：管理楼A232
专家简介： 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 served in many editorial boards including POM and Omega. 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.
报告摘要：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.
