A Comparison of Model Selection Methods for Multi-class Support Vector Machines

Huaqing Li, Feihu Qi, Shaoyu Wang

Abstract: Model selection plays a key role in the performance of support vector machines (SVMs). At present, nearly all researches are based on binary classification and focus on how to estimate the generalization performance of SVMs effectively and efficiently. For problems with more than two classes, where a classifier is typically constructed by combining several binary SVMs [8], most researchers simply select all binary SVM models simultaneously in one hyper-parameter space. Though this all-in-one method works well, there is another choice – the one-in-one method where each binary SVM model is selected independently and separately. In this paper, we compare the two methods for multi-class SVMs with the one-against-one strategy [8]. Their properties are discussed and their performance is analyzed based on experimental results.