Events

Dr. Xin Tong
Associate Professor of Data Sciences and Operations
Marshall School of Business
University of Southern California

In many binary classification applications, such as disease diagnosis and fraud detection, practitioners commonly face the need to limit type I error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (that is, the conditional probability of misclassifying a class 1 observation as class 0) while enforcing a user-specified, upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha.  This talk introduces the speaker and coauthors’ work on NP classification theory, algorithms, and their applications.