报告题目：带有逻辑表达的两步式贝叶斯多分类方法 / Two-Step Bayesian Multiple Classifications with Logic Expressions
报告人：吴文嵩 博士
报告人单位：美国佛罗里达国际大学
时间：2018年9月20日（周四）下午13:30-14:30
地点：教2-314（bat365在线官网入口会议室）
主办单位：bat365在线官网入口报告内容：In this presentation we consider a two-class classification problem, where the goal is to predict the class membership of M units based on the values of high-dimensional categorical predictor variables as well as both the values of predictor variables and the class membership of other N independent units. We focus on applying generalized linear regression models with Boolean expressions of categorical predictors. We consider a Bayesian and decision-theoretic framework, and develop a general form of Bayes multiple classification function (BMCF) with respect to a class of cost-weighted loss functions. In particular, the loss function pairs such as the proportions of false positives and false negatives, and (1-sensitivity) and (1-specificity), are considered. The best Boolean expressions are selected by a data driven procedure, where the candidates are first selected by Apriori Algorithm, an efficient algorithm for detecting association rules and frequent patterns, and the final expressions are selected by Bayesian model averaging. This two-step procedure will reduce model uncertainty in model selection and retain computational efficiency. The results will be illustrated via simulations and on a Lupus disease（红斑狼疮）dataset.
报告人简介：吴文嵩博士2011年在美国南卡罗莱纳大学University of South Carolina取得统计博士学位，同年在美国佛罗里达国际大学Florida International University数学与统计系任助理教授，2017年任副教授（终身）。研究方向包括贝叶斯多决策理论和计算，统计模型选择和模型平均，高维数据分析和统计学习，生物大数据的统计应用等。目前担任Journal of Probability and Statistical Sciences副主编。