Multiple-class classification: Ordinal and categorical labels. Issue 10 (26th November 2017)

• Record Type: Journal Article
• Authors: Chang, Yuan-chin Ivan
• Is Part Of: Communications in statistics. Volume 46:Issue 10(2017)
• Page Start: 7561
• Page End: 7581
• Publication Date: 2017-11-26
• Subjects: classification -- imbalanced data -- multiple-class -- ordinal response; Primary 62H30 -- Secondary 62J12; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1242732
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: We study multiple-class classification problems. Both ordinal and categorical labeled cases are discussed. The common approaches for multiple-class classification are built on binary classifiers, in which one-versus-one and one-versus-rest are typical approaches. When the number of classes is large, then these binary-classifier-based methods may suffer from either computational costs or the highly imbalanced sample sizes in their training stage. In order to alleviate the computational burden and the imbalanced training data issue in multiple-class classification problems, we propose a method that has competitive performance and retains the ease of model interpretation, which is essential for a prognostic/predictive model.