Sequential Neutral Zone Classification

• Activity Number: 336
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Hyunkyoung Kim*+ and Daniel Jeske
• Companies: UCR and University of California, Riverside
• Keywords: Sequential classification ; Neutral zone classifier ; Longitudinal data ; Optimal decision boundaries

Abstract

Neutral zone classifiers were developed to deal with ambiguity in classification problems. The classifier allows a region of neutrality if there is insufficient evidence to assign an object into one of the groups. Previous work has dealt with single-stage classification decisions. We extend the concept of neutral zone classification to multi-stage contexts, such as longitudinal data paradigms. Decision boundaries are derived to minimize the expected misclassification cost. Misclassification rates and expected stopping times are investigated. We also propose two algorithms for optimal decision boundaries.