Two pattern classifiers for interval data based on binary regression models

This paper introduces two classifiers for interval symbolic data based on logit and probit regression models, respectively. Each example of the learning set is described by a feature vector, for which each feature value is an interval and a binary response that defines the class of this example. For each classifier two versions are considered. First fits a classic binary regression model conjointly on the lower and upper bounds of the interval values assumed by the variables in the learning set. Second fits a classic binary regression model separately on the lower and upper bounds of the intervals. The prediction of the class for new examples is accomplished from the computation of the posterior probabilities of the classes. To show the usefulness of this method, examples with synthetic symbolic data sets with overlapping classes are considered.