Title A Generalized Logistic Link Function for Cumulative Link Models in Ordinal Regression
External publication No
Means Neural Process Letters
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 1
JCR Impact 1.78700
SJR Impact 0.51000
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010747996&doi=10.1007%2fs11063-017-9589-3&partnerID=40&md5=e70aeda0ea491b8f34911548832ec946
Publication date 01/08/2017
ISI 000406397000015
Scopus Id 2-s2.0-85010747996
DOI 10.1007/s11063-017-9589-3
Abstract Ordinal regression is a kind of regression analysis used for predicting an ordered response variable. In these problems, the patterns are labelled by a set of ranks with an ordering among the different categories. The most common type of ordinal regression model is the cumulative link model. The cumulative link model relates an unobserved continuous latent variable with a monotone link function. Logit and probit functions are examples of link functions used in cumulative link models. In this paper, a novel generalized link function based on a generalization of the logistic distribution is proposed. The generalized link function proposed is able to reproduce other different link functions by changing two real parameters: and . The generalized link function has been included in a cumulative link model where the latent function is determined by a standard neural network in order to test the performance of the proposal. For this model, a reformulation of the tunable thresholds and distribution parameters was applied to convert the constrained optimization problem into an unconstrained optimization problem. Experimental results demonstrate that our proposed approach can achieve competitive generalization performance.
Keywords Ordinal regression; Cumulative link models; Neural networks; Generalized logistic distribution
Universidad Loyola members