A Classifier for Interval Symbolic Data Based on A Multi-Class Probit Model
نویسندگان
چکیده
This paper introduces a new classifier based on the multi-class probit regressiton model for interval symbolic data. Each pattern of the learning set is described by a vector o features. Each feature is represented by an interval. Two versions of this classifier are considered. The first version fits a multi-class probit regression model conjointly on the lower and upper bounds of the interval values assumed by the variables in the learning set. The second version fits a multi-class probit model on the lower and upper bounds of the intervals separately. Allocation of new examples is accomplished by computing the posterior probabilities for each class. To show the usefulness of this method, experiments are made on two synthetic symbolic data sets, both with different degrees of orverlapping classes. The assessment of the proposed classification method is based on estimation of average behavior of error rate.
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