Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 305 - Asymmetry in Objectives and Samples for Classification and Multiple Testing
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: WNAR
Abstract #312616
Title: Neyman-Pearson Classification Under Label Noise
Author(s): Shunan Yao* and Bradley Rava and Xin Tong and Gareth James
Companies: University of Southern California and University of Southern California and University of Southern California and University of Southern California
Keywords: Neyman-Pearson paradigm; label noise; assymetric classification; corruption-adjusted algorithm

The existence of label noise in data has been a long-lasting problem in many classification applications. It is shown that errors in labels may affect the effectiveness of many widely used classification methods. In this work, we focus on label noise in asymmetric binary classification, specifically Neyman-Pearson paradigm. Suppose every observation has a true label and a corrupted label, then under the assumption of the mixture model for the corrupted classes, then with respect to true label, usual Neyman-Pearson classifiers will yield an unnecessary small type I error and possibly a type II error too large to be accepted. Thus, we propose a theory-backed algorithm that can keep the type I error under control with high probability while adjusting the effect of label noise.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program