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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...
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