ACCEPTED MANUSCRIPT Multiple-class classification: ordinal and categorical labels Yuan-chin Ivan Chang Institute of Statistical Science Academia Sinica Taipei, Taiwan 11529 ycchang@stat.sinica.edu.tw Key Words: Classification, multiple-class, ordinal response, imbalanced data 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. 1 ACCEPTED MANUSCRIPT