Support vector machines for interval discriminant analysis
Neurocomputing
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Our investigation aims at extending kernel methods to interval data mining and using graphical methods to explain the obtained results. Interval data type can be a good way to aggregate large datasets into smaller ones or to represent data with uncertainty. No algorithmic changes are required from the usual case of continuous data other than the modification of the Radial Basis Kernel Function evaluation. Thus, kernel-based algorithms can deal easily 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 support vector machines results. The user has a better understanding of the models' behaviour.