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Representing ordinal input variables in the context of ordinal classification
2016 International Joint Conference on Neural Networks (IJCNN), 2016Ordinal input variables are common in many supervised and unsupervised machine learning problems. We focus on ordinal classification problems, where the target variable is also categorical and ordinal. In order to represent categorical input variables for measuring distances or applying continuous mapping functions, they have to be transformed to ...
Pedro Antonio Gutiérrez +3 more
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Classification trees for ordinal variables
Computational Statistics, 2007Classification trees growing algorithms are considered for the case when there is an ordering of the response variable values. New versions of the Gini-Simpson and Twoing criteria are proposed for the choice of the nodes to split, which are consistent with the ordering.
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2018
This chapter examines measures of association designed for two ordinal-level variables that are based on pairwise comparisons of differences between rank scores. Included in Chap. 5 are Kendall’s τa and τb measures of ordinal association, Stuart’s τc measure, Goodman and Kruskal’s γ measure, Somers’ dyx and dxy measures, Kim’s dy⋅x and dx⋅y measures ...
Kenneth J. Berry +2 more
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This chapter examines measures of association designed for two ordinal-level variables that are based on pairwise comparisons of differences between rank scores. Included in Chap. 5 are Kendall’s τa and τb measures of ordinal association, Stuart’s τc measure, Goodman and Kruskal’s γ measure, Somers’ dyx and dxy measures, Kim’s dy⋅x and dx⋅y measures ...
Kenneth J. Berry +2 more
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On the Nonlinearity of Homogeneous Ordinal Variables
2011The paper aims at evaluating the nonlinearity existing in homogeneous ordinal data with a one-dimensional latent variable, using Linear and NonLinear Principal Components Analysis. The results of a simulation study with Probabilistic and Monte Carlo gauges show that, when variables are linearly related, a source of nonlinearity can affect each single ...
CARPITA, Maurizio, MANISERA, Marica
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1989
Abstract In the analysis of categorical data, ordinal variables are commonly encountered. The categories are known to have an order but knowledge of the scale is insufficient to consider them as forming a metric. Although they may be treated simply as nominal categories, as in the first two chapters, valuable information is being lost ...
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Abstract In the analysis of categorical data, ordinal variables are commonly encountered. The categories are known to have an order but knowledge of the scale is insufficient to consider them as forming a metric. Although they may be treated simply as nominal categories, as in the first two chapters, valuable information is being lost ...
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DISSONANCE — A MEASURE OF VARIABILITY FOR ORDINAL RANDOM VARIABLES
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2001We look at the issue of obtaining a variance like measure associated with probability distributions over ordinal sets. We call these dissonance measures. We specify some general properties desired in these dissonance measures. The centrality of the cumulative distribution function in formulating the concept of dissonance is pointed out.
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HOMOGENEOUS FORMS IN TWO ORDINAL VARIABLES
Mathematical Logic Quarterly, 1984In this paper some ordinal-valued ''diophantine'' equations are studied. If t, \(c_{r,s}\) are finite, the number of y's, for which \[ FD(x,y)=x^ tc_{t,0}+x^{t-1}yc_{t-1,1}+...+y^ tc_{0,t}=\alpha \] is solvable (\(\alpha\) is a fixed infinite ordinal), is finite. If such a y is infinite, x is the smallest solution, then \(FD(x+z,y)=\alpha\) iff \(z+y=y\
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On the Impact of Misclassification in an Ordinal Exposure Variable
Epidemiologic Methods, 2014Abstract Say that interest focuses on the relationship between an exposure variable and an outcome variable; however, the exposure variable is subject to measurement error. While exceptions have been identified, in almost all circumstances nondifferential measurement error leads to attenuated regression coefficients and lost power to ...
Wang, Dongxu, Gustafson, Paul
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Regression for ordinal variables without underlying continuous variables
Information Sciences, 2006Several techniques exist nowadays for continuous (i.e. numerical) data analysis and modeling. However, although part of the information gathered by companies, statistical offices and other institutions is numerical, a large part of it is represented using categorical variables in ordinal or nominal scales.
Vicenç Torra +3 more
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