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Advances in Data Analysis and Classification

Erschienen in:

12.02.2019 | Regular Article

Discriminant analysis for discrete variables derived from a tree-structured graphical model

verfasst von: Gonzalo Perez-de-la-Cruz, Guillermina Eslava-Gomez

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 4/2019

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Abstract

The purpose of this paper is to illustrate the potential use of discriminant analysis for discrete variables whose dependence structure is assumed to follow, or can be approximated by, a tree-structured graphical model. This is done by comparing its empirical performance, using estimated error rates for real and simulated data, with the well-known Naive Bayes classification rule and with linear logistic regression, both of which do not consider any interaction between variables, and with models that consider interactions like a decomposable and the saturated model. The results show that discriminant analysis based on tree-structured graphical models, a simple nonlinear method including only some of the pairwise interactions between variables, is competitive with, and sometimes superior to, other methods which assume no interactions, and has the advantage over more complex decomposable models of finding the graph structure in a fast way and exact form.

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Titel: Discriminant analysis for discrete variables derived from a tree-structured graphical model
verfasst von: Gonzalo Perez-de-la-Cruz
Guillermina Eslava-Gomez
Publikationsdatum: 12.02.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 4/2019
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-019-00352-z

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