The series hosts a seminar every other week on current research topics. The seminar often features an invited guest speaker and occasionally local faculty members, students or others affiliated with the department. The usual time of the seminar is 3:30-4:30 pm on Fridays. Professors Tatiyana V Apanasovich ([email protected]), Qing Pan ([email protected]) and Emre Barut ([email protected] ) are the Seminar Series Coordinators.

Upcoming Seminar

Date: Friday, October 12th, 11:00am-12:00pm

Location: Duques Hall, Room 152

Title: Neyman-Pearson Classification

Speaker: Xin Tong, University of Southern California, Department of Data Sciences and Operations

Abstract: In many binary classification applications, such as disease diagnosis and spam 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 an 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 algorithms and their applications and raises current challenges under the NP paradigm.