Decision Trees for Functional Variables

Classification problems with functionally structured input variables arise naturally in many applications. In a clinical domain, for example, input variables could include a time series of blood pressure measurements. In a financial setting, different time series of stock returns might serve as predictors. In an archaeological application, the 2D profile of an artifact may serve as a key input variable. In such domains, accuracy of the classifier is not the only reasonable goal to strive for; classifiers that provide easily interpretable results are also of value. In this work, we present an intuitive scheme for extending decision trees to handle functional input variables. Our results show that such decision trees are both accurate and readily interpretable.