Ordinal regression

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

Quantitative Research Approach

Abstract: The hypotheses for the quantitative study that forms the second phase of the research have been developed based on the qualitative expert interviews and the PO.

Multilevel relevance judgment, loss function, and performance measure in image retrieval

Abstract: Most learning algorithms for image retrieval are based on dichotomy relevance judgement (relevance and non-relevance), though this measurement of relevance is too coarse. To better identify the user needs and preference, a good retrieval system should be able to handle multilevel relevance judgement. In this paper, we focus on relevance feedback with multilevel relevance judgment. We consider relevance feedback as an ordinal regression problem, and discuss its properties and loss function. Since traditional performance measures such as precision and recall are based on dichotomy relevance judgment, we adopt a performance measure that is based on the preference of one image to another one. Furthermore, we develop a new relevance feedback scheme based on a support vector learning algorithm for ordinal regression. Our solution is tested on real image database, and promising results are achieved.

Censoring-Aware Deep Ordinal Regression for Survival Prediction from Pathological Images

Abstract: Survival prediction is a typical task in computer-aided diagnosis with many clinical applications. Existing approaches to survival prediction are mostly based on the classic Cox model, which mainly focus on learning a hazard or survival function rather than the survival time, largely limiting their practical uses. In this paper, we present a Censoring-Aware Deep Ordinal Regression (CDOR) to directly predict survival time from pathological images. Instead of relying on the Cox model, CDOR formulates survival prediction as an ordinal regression problem, and particularly introduces a censoring-aware loss function to train the deep network in the presence of censored data. Experiment results on publicly available dataset demonstrate that, the proposed CDOR can achieve significant higher accuracy in predicting survival time.

Modeling item nonresponse in questionnaires

Abstract: The statistical analysis of empirical questionnaire data can be hampered by the fact that not all questions are answered by all individuals. In this paper we propose a simple practical method to deal with such item nonresponse in case of ordinal questionnaire data, where we assume that item nonresponse is caused by an incomplete set of answers between which the individuals are supposed to choose. Our statistical method is based on extending the ordinal regression model with an additional category for nonresponse, and on investigating whether this extended model describes and forecasts the data well. We illustrate our approach for two questions from a questionnaire held amongst a sample of clients of a financial investment company.

Nonparametric Predictive Inference for Ordinal Data

Abstract: Nonparametric predictive inference (NPI) is a powerful frequentist statistical framework based only on an exchangeability assumption for future and past observations, made possible by the use of lower and upper probabilities. In this article, NPI is presented for ordinal data, which are categorical data with an ordering of the categories. The method uses a latent variable representation of the observations and categories on the real line. Lower and upper probabilities for events involving the next observation are presented, and briefly compared to NPI for non ordered categorical data. As application, the comparison of multiple groups of ordinal data is presented.