A pattern classifier for interval-valued data based on multinomial logistic regression model

Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. This paper introduces a multinomial logistic regression method for interval-valued data in order to classify items described by interval-valued variables into a pre-defined number of a priori classes. Applications of the proposed approach on real as well as synthetic interval-valued data sets showed the usefulness of this approach.