Construction, visualization and application of neutral zone classifiers. (May 2020)

• Record Type: Journal Article
• Authors: Jeske, Daniel R; Zhang, Zhiwei; Smith, Steven
• Is Part Of: Statistical methods in medical research. Volume 29:Number 5(2020)
• Page Start: 1420
• Page End: 1433
• Publication Date: 2020-05
• Subjects: Statistical classification -- logistic regression -- neutral zones -- ROC curve -- prostate cancer; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280219863823
• Languages: English
• ISSNs: 0962-2802

Abstract

When the potential for making accurate classifications with a statistical classifier is limited, a neutral zone classifier can be constructed by adding a no-decision option as a classification outcome. We show how a neutral zone classifier can be constructed from a receiving operating characteristic (ROC) curve. We extend the ROC curve graphic to highlight important performance characteristics of a neutral zone classifier. Additional utility of neutral zone classifiers is illustrated by showing how they can be incorporated into the first stage of a two-stage classification process. At the first stage, a classification is attempted from easily collected or inexpensive features. If the classification falls into the neutral zone, additional relatively more expensive features can be obtained and used to make a definitive classification at the second stage. The methods discussed in the paper are illustrated with an application pertaining to prostate cancer.