Cascading for nominal data

Abstract

In pattern recognition many methods need numbers as inputs. Using nominal datasets with these methods requires to transform such data into numerical. Usually, this transformation consists in encoding nominal attributes into a group of binary attributes (one for each possible nominal value). This approach, however, can be enhanced for certain methods (e.g., those requiring linear separable data representations). In this paper, different alternatives are evaluated for enhancing SVM (Support Vector Machine) accuracy with nominal data. Some of these approaches convert nominal into continuous attributes using distance metrics (i.e., VDM (Value Difference Metric)). Other approaches combine the SVM with other classifier which could work directly with nominal data (i.e., a Decision Tree). An experimental validation over 27 datasets shows that Cascading with an SVM at Level-2 and a Decision Tree at Level-1 is a very interesting solution in comparison with other combinations of these base classifiers, and when compared to VDM. © Springer-Verlag Berlin Heidelberg 2007.