Embedding Proximal Support Vectors into Randomized Trees

By embedding multiple proximal SVM classifiers into a binary tree architecture, it is possible to turn an arbitrary multi-classes problem into a hierarchy of binary classifications. The critical issue then consists in determining in each node of the tree how to aggregate the multiple classes into a pair of say overlay classes to discriminate. As a fundamental contribution, our paper proposes to deploy an ensemble of randomized trees, instead of a single optimized decision tree, to bypass the question of overlay classes definition. Empirical results on various datasets demonstrate a significant gain in accuracy both compared to ’one versus one’ SVM solutions and to conventional ensemble of decision trees classifiers.