Comparison of a Novel Combined ECOC Strategy with Different Multiclass Algorithms Together with Parameter Optimization Methods

摘要

In this paper we consider multiclass learning tasks based on Support Vector Machines (SVMs). In this regard, currently used methods are One-Against-All or One-Against-One, but there is much need for improvements in the field of multiclass learning. We developed a novel combination algorithm called Comb-ECOC, which is based on posterior class probabilities. It assigns, according to the Bayesian rule, the respective instance to the class with the highest posterior probability. A problem with the usage of a multiclass method is the proper choice of parameters. Many users only take the default parameters of the respective learning algorithms (e.g. the regularization parameter C and the kernel parameter gamma). We tested different parameter optimization methods on different learning algorithms and confirmed the better performance of One-Against-One versus One-Against-All, which can be explained by the maximum margin approach of SVMs.