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Evaluating classification accuracy for modern learning approaches
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报告题目:Evaluating classification accuracy for modern learning approaches

报告人:栗家量、副教授、新加坡国立大学

照片:

邀请人:刘三阳 教授

报告时间:5月14日下午3:30—4:30

报告地点:信远楼II 206报告厅

报告人简介:栗家量,新加坡国立大学副教授,副系主任(科研),长期从事数理统计学、生物统计学的理论和应用研究,主要研究领域为个性化医疗、医学诊断、风险预测、统计学习、生存分析等。以第一作者或通讯作者身份发表包括The Annals of Statistics, Journal of the American Statistical Association在内的统计学顶级期刊文章,以及Statistics in Medicine, Biometrics, Biostatistics, Statistical Methods in Medical Research等生物、医学统计顶级期刊文章数十余篇,连同合作发表的文章,总数近140篇。目前担任Biometrics, Lifetime Data Analysis等国际权威期刊的Associate Editor。

报告摘要: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.

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