Oblique Decision Tree Ensemble via Multisurface Proximal Support Vector Machine

A new approach to generate oblique decision tree ensemble is proposed wherein each decision hyperplane in the internal node of tree classifier is not always orthogonal to a feature axis. All training samples in each internal node are grouped into two hyper-classes according to their geometric properties based on a randomly selected feature subset. Then multisurface proximal support vector machine is employed to obtain two clustering hyperplanes where each hyperplane is generated such that it is closest to one group of the data and as far as possible from the other group. Then, one of the bisectors of these two hyperplanes is regarded as the test hyperplane for this internal node. Several regularization methods have been applied to handle the small sample size problem as the tree grows. The effectiveness of the proposed method is demonstrated by 44 real-world benchmark classification data sets from various research fields. These classification results show the advantage of the proposed approach in both computation time and classification accuracy.