Ordinal regression based on data relationship - Liu, Yanzhu (2019)
Ordinal regression is a supervised learning problem which aims to classify instances into ordinal categories. It is different from multi-class classification because there is an ordinal relationship between the categories. Moreover, it is different from metric regression because the target values to be predicted are discrete and the distances between different categories are not defined. Ordinal regression is an active research area because of numerous governmental, commercial and scientific applications, such as quality assessment, disease grading, credit rating, and risk stratification. The main challenge of ordinal regression is to model the ordinal information carried by the labels. Traditionally, there are two angles to tackle the ordinal regression problem: from metric regression perspective and classification perspective. However, most of existing works under both above categories are pointwise methods, in which the relationship between pairs or lists of data points is not explored sufficiently. Furthermore, learning models, especially deep neural network based models, on small datasets is challenging, but many real-world ordinal regression problems are in fact small data problems. The aim of this research is to propose ordinal regression algorithms by exploring data relationship and give consideration to suitability for small datasets and scalability for large datasets.