A Robust Prediction Method for Interval Symbolic Data

Abstract

This paper introduces a robust prediction method for symbolic interval data based on the simple linear regression methodology. Each example of the data set is described by feature vector, for which each feature is an interval. Two classic robust regression models are fitted, respectively for range and mid-points of the interval values assumed by the variables in the data set. The prediction of the lower and upper bounds of the new intervals is performed from these fits. To validate this model, experiments with a synthetic interval data set and an application with a cardiology interval-valued data set are considered. The fit and prediction qualities are assessed by a pooled root mean square error measure calculated from learning and test data sets, respectively.