Learning ordinal data

Classification is an important topic in statistical learning. The goal of classification is to build a predictive model from the training dataset for the class label of an observation. It is commonly assumed that the class labels are unordered. However, in many real applications, there exists an intrinsic ordinal relation between the class labels. Examples of these include cancer patients grouped in early, mediocre, and terminal stages, customers grouped into low, middle, and high credit levels, and experimental subjects enriched with different amounts of bacterial. In this article, we focus on the classification problem for ordinal data and introduce the theoretical setup of the problem. We review both traditional and modern methods in learning ordinal data. In particular, we emphasize the trade-off between model flexibility and interpretability. Lastly, we discuss some issues regarding ordinal data learning, including an appropriate loss function for this problem. WIREs Comput Stat 2015, 7:341-346. doi: 10.1002/wics.1357 For further resources related to this article, please visit the WIREs website.