Multi-class from Binary - Divide to conquer

Several researchers have proposed effective approaches for binary classification in the last years. We can easily extend some of those techniques to multi-class. Notwithstanding, some other powerful classifiers (e.g., SVMs) are hard to extend to multi-class. In such cases, the usual approach is to reduce the multi-class problem complexity into simpler binary classification problems (divide-and-conquer). In this paper, we address the multi-class problem by introducing the concept of affine relations among binary classifiers (dichotomies), and present a principled way to find groups of high correlated base learners. Finally, we devise a strategy to reduce the number of required dichotomies in the overall multi-class process.