Kernel-based Methods and Visualization for Interval Data Analysis

When large datasets are aggregated into smaller data sizes we need more complex data tables e.g. interval type instead of standard ones. Our investigation in this paper aims at extending kernel methods to interval data analysis and using graphical methods to explain the obtained results. No algorithmic changes are required from the usual case of continuous data other than the modification of the RBF kernel evaluation. Thus, kernel-based algorithms can deal with interval data. The numerical test results with real and artificial datasets show that the proposed methods have given promising performance. We also use interactive graphical decision tree algorithms and visualization techniques to give an insight into SVM results. The user deeply understands the models’ behaviour towards data.