In regression models for ordinal response, each covariate can be equipped with either a simple, global effect or a more flexible and complex effect which is specific to the response categories. Instead of a priori assuming one of these effect types, as is done in the majority of the literature, we argue in this paper that effect type selection shall be data-based. For this purpose, we propose a...

Objective. This article examines the black-white gap in confidence in education in the United States and how the gap has changed over time. Method. The study uses ordinal logit regression on General Social Surveys (1974–2002). Results. Whites have less confidence in education, partly because whites tend to have higher levels of education, income, and conservatism, and are more likely to be affi...

Ordinal regression aims to classify instances into ordinal categories. As with other supervised learning problems, learning an effective deep ordinal model from a small dataset is challenging. This paper proposes a new approach which transforms the ordinal regression problem to binary classification problems and uses triplets with instances from different categories to train deep neural network...

Instead of traditional (nominal) classification we investigate the subject of ordinal classification or ranking. An enhanced method based on an ensemble of Support Vector Machines (SVM’s) is proposed. Each binary classifier is trained with specific weights for each object in the training data set. Experiments on benchmark datasets and synthetic data indicate that the performance of our approach...

Ordinal regression learning has characteristics of both multi-class classification and metric regression because labels take ordered, discrete values. In applications of ordinal regression, the misclassification cost among the classes often differs and with different misclassification costs the common performance measures are not appropriate. Therefore we extend ROC analysis principles to ordin...