Ordinal regression

About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.

Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index Range Estimation

Abstract: Ordinal regression aims to classify instances into ordinal categories. In this paper, body mass index (BMI) category estimation from facial images is cast as an ordinal regression problem. In particular, noisy binary search algorithms based on pairwise comparisons are employed to exploit the ordinal relationship among BMI categories. Comparisons are performed with Siamese architectures, one of which uses the Bradley-Terry model probabilities as target. The Bradley-Terry model is an approach to describe probabilities of the possible outcomes when elements of a set are repeatedly compared with one another in pairs. Experimental results show that our approach outperforms classification and regression-based methods at estimating BMI categories.

An Effective Tree-Based Algorithm for Ordinal Regression

Abstract: Recently ordinal regression has attracted much interest in machine learning. The goal of ordinal regression is to assign each instance a rank, which should be as close as possible to its true rank. We propose an effective tree-based algorithm, called Ranking Tree, for ordinal regression. The main advantage of Ranking Tree is that it can group samples with closer ranks together in the process of tree learning. This approach is compared with original decision tree. Experiments on some synthetic and real-world datasets show that Ranking Tree outperforms original decision tree in terms of speed and accuracy as well as robustness.

A Multi-class Classification Algorithm based on Ordinal Regression Machine

Abstract: Multi-class classification is an important and on-going research subject in machine learning. In this paper, we propose a multi-class classification algorithm based on ordinal regression algorithm using 3-class classification. This algorithm is similar to algorithm K-SVCR and algorithm nu-K-SVCR, but it includes fewer parameters. Another advantage of our algorithm is that, for the K-class classification problem, our algorithm can be extended to using p-class classification with 2 les p les K. Numerical experiments on artificial data sets and benchmark data sets show that the algorithm is reasonable and effective

Proportional odds logistic regression—effective means of dealing with limited uncertainty in dichotomizing clinical outcomes

Abstract: Classifying a measurable clinical outcome as a dichotomous variable often involves difficulty with borderline cases that could fairly be assigned either of the two binary class memberships. In such situations the indicated class membership is often highly subjective and subject to, for instance, a measurement error. In other situations the intermediate level of a three-level ordinal factor may sometimes be explicitly reserved for cases which could likely belong to either of the two binary classes. Such indefinite readings are often eliminated from the statistical analysis. In this article we review conceptual and methodological aspects of employing proportional odds logistic regression for a three level ordinal factor as a suitable alternative to ordinary logistic regression when dealing with limited uncertainty in classifying clinical outcome as a binary variable.