We present four new feature selection methods for ordinal regression and test them against four different baselines on two large datasets of product reviews.

Abstract In binary and ordinal regression one can distinguish between a location component scaling component. While the former determines within range of response categories, indicates variance heterogeneity. particular since it has been demonstrated that misleading effects occur if ignores presence component, is important to account for potential in model, which not possible available recursiv...

We consider imprecise evaluation of alternatives in multiple criteria ranking problems. The imprecise evaluations are represented by n-point intervals which are defined by the largest interval of possible evaluations and by its subintervals sequentially nested one in another. This sequence of subintervals is associated with an increasing sequence of plausibility, such that the plausibility of a...

Ordinal models can be seen as being composed from simpler, in particular binary models. This view on ordinal allows to derive a taxonomy of that includes basic regression models, with more complex parameterizations, the class hierarchically structured and recently developed finite mixture The overview is given covers existing shows how extended account for further effects explanatory variables....

In the present informational era, with the continue extension of embedded computing systems, the demand of faster and robust image descriptors is an important issue. However, image representation and recognition is an open problem. The aim of the paper is to embrace ordinal measurements for image analysis and to apply the concept for a real problem, such as biometric identification. Biometrics ...

We propose in this article a Composite Logistic Regression (CLR) approach for ordinal panel data regression. The new method transforms the original ordinal regression problem into a number of binary ones. Thereafter, the method of conditional logistic regression (Chamberlain, 1984; Wooldridge, 2001; Hsiao, 2003) can be directly applied. As a result, the new method allows the unobserved subject ...

The multicriteria method MUSA (MUlticriteria Satisfaction Analysis) for measuring and analysing customer satisfaction is presented in this paper. The MUSA method is a preference disaggregation model following the principles of ordinal regression analysis (inference procedure). The integrated methodology evaluates the satisfaction level of a set of individuals (customers, employees, etc.) based ...