学术报告

报告题目：Evaluating classification accuracy for modern learning approaches

报告时间：2019年11月19日10:00--11:00

报告地点：25教学楼14楼学术报告厅

摘要：Deep learning neural network models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are novel and attractive artificial intelligence computing tools. However, evaluation of the performance of these methods is not readily available for practitioners yet. We provide a tutorial for evaluating classification accuracy for various state-of-the-art learning approaches, including familiar shallow and deep learning methods. For qualitative response variables with more than two categories, many traditional accuracy measures such as sensitivity, specificity, and area under the receiver operating characteristic curve are not applicable and we have to consider their extensions properly. In this paper, a few important statistical concepts for multicategory classification accuracy are reviewed and their utilities for various learning algorithms are demonstrated with real medical examples. We offer problem-based R code to illustrate how to perform these statistical computations step by step. We expect that such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics.

报告人简介：栗家量，新加坡国立大学副教授，副系主任（科研），于2001年毕业于中国科学技术大学获得理学学士学位；2006年在美国威斯康星大学-麦迪逊分校(University of Wisconsin, Madison)获得统计学博士学位。他的主要研究领域为非参数统计、函数型数据分析、高维统计推断等，已发表包含Annals of Statistics, Journal of the American Statistical Association，Journal of Econometric等顶级期刊在内的论文60余篇，目前担任Biometrics, Lifetime Data Analysis等国际权威期刊的Associate Editor。