Experiments on Solving Multiclass Learning Problems by n2-classifier

The paper presents an experimental study of solving multiclass learning problems by a method called n2-classifier. This approach is based on training (n2 - n)/2 binary classifiers - one for each pair of classes. Final decision is obtained by a weighted majority voting rule. The aim of the computational experiment is to examine the influence of the choice of a learning algorithm on a classification performance of the n2-classifier. Three different algorithms are n2-classifier. decision trees, neural networks and instance based learning algorithm.