The purpose of this paper is to introduce a new distance metric learning algorithm for ordinal regression. Ordinal regression addresses the problem predicting classes which there natural ordering, but real distances between are unknown. Since walks fine line standard and classification, it common pitfall either apply regression-like numerical treatment variables or underrate information applyin...

In this paper, we address the Multi-Instance-Learning (MIL) problem when bag labels are naturally represented as ordinal variables (Multi–Instance–Ordinal Regression). Moreover, we consider the case where bags are temporal sequences of ordinal instances. To model this, we propose the novel Multi-Instance Dynamic Ordinal Random Fields (MI-DORF). In this model, we treat instance-labels inside the...

INTRODUCTION We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical...

In this paper, we generalize the noisy-or model. The generalizations are three-fold. First, we allow parents to be multivalued ordinal variables. Second, parents can have both positive and negative influences on their common child. Third, we describe how the suggested generalization can be extended to multivalued child variables. The major advantage of our generalizations is that they require o...

Predictive equations were developed for 19 ecologically relevant streamflow characteristics within five major groups of flow variables (magnitude, ratio, frequency, variability, and date) for use in the Tennessee and Cumberland River basins using stepbackward regression. Basin characteristics explain 50% or more of the variation for 12 of the 19 equations. Independent variables identified throu...

We introduce McCullagh’s Proportional Odds as the foundation for modern Ordinal Regression approaches. Proportional Odds introduced the ideas of (1) mapping examples to the real number line, and (2) segmenting the real number line using a set of thresholds. We compare against two modern approaches to Ordinal Regression which use the framework established by Proportional Odds and find some surpr...

In this paper we focus on gaining insight into the residential satisfaction of households near highways, based on survey data collected among 1,225 respondents in the Netherlands living within 1,000 meters from a highway. Ordinal regression was used to study the impact of highway externalities on residential satisfaction. Moreover, we gained first insights into the reactions of people on highwa...