Logistic regression-based pattern classifiers for symbolic interval data

Authors:
Renata M. C. R. de Souza;Diego C. F. Queiroz;Francisco José A. Cysneiros
Affiliations:
Universidade Federal de Pernambuco, Centro de Informática, CEP 50740-540, Av. Prof. Luiz Freire, s/n, Cidade Universitária, Recife, PE, Brazil;Universidade Federal de Pernambuco, Centro de Informática, CEP 50740-540, Av. Prof. Luiz Freire, s/n, Cidade Universitária, Recife, PE, Brazil;Universidade Federal de Pernambuco, Departamento de Estatística, CCEN, CEP 50740-540, Av. Prof. Luiz Freire, s/n, Cidade Universitaria, Recife, PE, Brazil
Venue:
Pattern Analysis & Applications
Year:
2011

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Abstract

This paper introduces different pattern classifiers for interval data based on the logistic regression methodology. Four approaches are considered. These approaches differ according to the way of representing the intervals. The first classifier considers that each interval is represented by the centres of the intervals and performs a classic logistic regression on the centers of the intervals. The second one assumes each interval as a pair of quantitative variables and performs a conjoint classic logistic regression on these variables. The third one considers that each interval is represented by its vertices and a classic logistic regression on the vertices of the intervals is applied. The last one assumes each interval as a pair of quantitative variables, performs two separate classic logistic regressions on these variables and combines the results in some appropriate way. Experiments with synthetic data sets and an application with a real interval data set demonstrate the usefulness of these classifiers.