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大连理工大学海天学者李晓白教授学术报告

2018年06月11日 17:04  点击:[]

报告题目:Support Vector Regression for Handling Strategically Missing Data

报告人:李晓白  教授

邀请人:胡祥培  教授

时间及地点:2018621日上午1000-1100,管经新大楼B312

报告人简介:Dr. Xiaobai Li is a Professor of Information Systems in the Department of Operations and Information Systems at the University of Massachusetts Lowell, USA. Li’s research focuses on data mining and analytics, data privacy, and information economics. He has received funding for his research from National Institutes of Health (NIH) and National Science Foundation (NSF). His work has appeared in Management Science, Information Systems Research, MIS Quarterly, Operations Research, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics, Communications of the ACM, INFORMS Journal on Computing, Decision Support Systems, and European Journal of Operational Research, among others.He is serving as an Associate Editor for Information Systerms Research,Decision Support Systerms,and ACM Journal of Data and Information Quality.

报告摘要:We study strategically missing data problems in predictive analytics with regression. In many real-world situations, such as financial reporting, college admission, job application, and marketing advertisement, data providers often hide certain information on purpose in order to gain a favorable outcome. It is important for the decision maker to have a mechanism to deal with such strategic behaviors. We propose a novel approach, based on the Support Vector Regression (SVR) technique, to handle strategically missing data in regression prediction. The proposed method derives the imputed values of missing data based on the margins of the SVR models. It provides incentives for the data providers to disclose their true information. We show that imputation errors for the missing values are minimized under some reasonable conditions. Furthermore, with the proposed method, the decision maker’s decision models will not be affected by strategic behaviors of data providers. An experimental study on real-world data demonstrates the effectiveness of the proposed approach.

 

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