A novel approach for generating fast multi-class SVM topologies with nested dichotomies
Ismail Uluturk, Ismail Uysal · 2016
Support vector machines (SVM), originally introduced as powerful binary classifiers, can also be used for multi-class recognition with the help of creative meta-learning strategies such as commonly used one-vs-rest, one-vs-one and majority voting. In this paper, we explore the potential of creating informed nested dichotomies based on clustering pseudo-labels and probability estimates generated a priori through separate supervised training. Across six diverse public datasets, we found that in majority of the cases the final tree structure is unbalanced with one-off elimination of a single class in each branch. When compared to traditional multi-class SVM topologies, the proposed method shows no statistically significant change in accuracy. However, the on-demand testing time is reduced quite substantially with a range of 2-to-25-fold decrease in the computational load compared to baseline classifiers across different datasets. Finally, the proposed algorithm can be adjusted for class frequencies in imbalanced datasets to improve testing time even further for applications such as letter recognition.