Topic
Ordinal regression
About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.
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TL;DR: The Necessary-preferenceenhanced Evolutionary Multiobjective Optimizer (NEMO) as mentioned in this paper combines an evolutionary multiobjective optimization with robust ordinal regression within an interactive procedure.
Abstract: This paper presents the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), which combines an evolutionary multiobjective optimization with robust ordinal regression within an interactive procedure. In the course of NEMO, the decision maker is asked to express preferences by simply comparing some pairs of solutions in the current population. The whole set of additive value functions compatible with this preference information is used within a properly modified version of the evolutionary multiobjective optimization technique NSGA-II in order to focus the search towards solutions satisfying the preferences of the decision maker. This allows to speed up convergence to the most preferred region of the Pareto-front.
38 citations
TL;DR: In this article, a new approach based on the use of a new sample scale obtained by ordering the original variable sample space according to some specific "dominance criteria" fixed on the basis of the monitored process characteristics is presented.
Abstract: The paper presents a new method for statistical process control when ordinal variables are involved. This is the case of a quality characteristic evaluated by an ordinal scale. The method allows a statistical analysis without exploiting an arbitrary numerical conversion of scale levels and without using the traditional sample synthesis operators (sample mean and variance). It consists of a different approach based on the use of a new sample scale obtained by ordering the original variable sample space according to some specific ‘dominance criteria’ fixed on the basis of the monitored process characteristics. Samples are directly reported on the chart and no distributional shape is assumed for the population (universe) of evaluations. Finally, a practical application of the method in the health sector is provided. Copyright © 2005 John Wiley & Sons, Ltd.
38 citations
TL;DR: In this article, the ordinal structure is taken into account by use of a difference penalty on adjacent dummy coefficients, and an alternative blockwise boosting procedure is proposed to select ordinally scaled independent variables in the classical linear model.
Abstract: Summary. Ordinal categorial variables arise commonly in regression modelling. Although the analysis of ordinal response variables has been well investigated, less work has been done concerning ordinal predictors. We consider so-called international classfication of functioning core sets for chronic widespread pain, in which many ordinal covariates are collected. The effect of specific international classification of functioning variables on a subjective measure of physical health is investigated, which requires strategies for variable selection. In this context, we propose methods for the selection of ordinally scaled independent variables in the classical linear model. The ordinal structure is taken into account by use of a difference penalty on adjacent dummy coefficients. It is shown how the group lasso can be used for the selection of ordinal predictors, and an alternative blockwise boosting procedure is proposed. Both methods are discussed in general, and applied to international classification of functioning core sets for chronic widespread pain.
38 citations
38 citations
TL;DR: A new preference disaggregation method for multiple criteria sorting problems, called DIS-CARD, using the ordinal regression approach to construct a model of DM’s preferences from preference information provided in terms of exemplary assignments of some reference alternatives, together with the above desired cardinalities.
Abstract: In this paper, we present a new preference disaggregation method for multiple criteria sorting problems, called DIS-CARD. Real-life experience indicates the need of considering decision making situations in which a decision maker (DM) specifies a desired number of alternatives to be assigned to single classes or to unions of some classes. These situations require special methods for multiple criteria sorting subject to desired cardinalities of classes. DIS-CARD deals with such a problem, using the ordinal regression approach to construct a model of DM’s preferences from preference information provided in terms of exemplary assignments of some reference alternatives, together with the above desired cardinalities. We develop a mathematical model for incorporating such preference information via mixed integer linear programming (MILP). Then, we adapt the MILP model to two types of preference models: an additive value function and an outranking relation. Illustrative example is solved to illustrate the methodology.
38 citations