In contrast to the standard machine learning tasks of classi cation and metric regression we investigate the problem of predicting variables of ordinal scale, a setting referred to as ordinal regression. The task of ordinal regression arises frequently in the social sciences and in information retrieval where human preferences play a major role. Also many multi{class problems are really problem...

Nowadays the area under the receiver operating characteristics (ROC) curve, which corresponds to the Wilcoxon–Mann–Whitney test statistic, is increasingly used as a performance measure for binary classification systems. In this article we present a natural generalization of this concept for more than two ordered categories, a setting known as ordinal regression. Our extension of the Wilcoxon–Ma...

Ordinal regression analysis is a convenient tool for analyzing ordinal response variables in the presence of covariates. In this paper we extend this methodology to the case of continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales measure subjects' perception of an ...

The adjacent-categories, continuation-ratio and proportional odds logit-link regression models provide useful extensions of the multinomial logistic model to ordinal response data. We propose fitting these models with a logarithmic link to allow estimation of different forms of the risk ratio. Each of the resulting ordinal response log-link models is a constrained version of the log multinomial...

Word knowledge is often partial, rather than all-or-none. In this paper, we describe a method for estimating partial word knowledge on a trial-by-trial basis. Users generate a free-form synonym for a newly learned word. We then apply a probabilistic regression model that combines features based on Latent Semantic Analysis (LSA) with features derived from a large-scale, multi-relation word graph...

We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression--the ORCHID (Ordinal Regressio...

musa is one of the novel techniques in csa, which lays its foundation on linear goal programming, developed for overcoming prior csa models’ weaknesses such as coping with ordinal nature of data and low fitness. employing a simple questionnaire, musa develops the interval scale and the level of satisfaction as well as determining its determinants in addition to several fruitful indices. this pa...

Support vector ordinal regression machine (SVORM) is an effective method for ordinal regression problem. Up to now, the SVORM implicitly assumes the training data to be known exactly. However, in practice, the training data subject to measurement noise. In this paper, we propose a robust version of SVORM. The robustness of the proposed method is validated by our preliminary numerical experiments.

Univariate or multivariate ordinal responses are often assumed to arise from a latent continuous parametric distribution, with covariate effects which enter linearly. We introduce a Bayesian nonparametric modeling approach for univariate and multivariate ordinal regression, which is based on mixture modeling for the joint distribution of latent responses and covariates. The modeling framework e...