Multiclass classifier based on boundary complexity

This paper presents several criteria for partition of classes for the support vector machine based hierarchical classification. Our clustering algorithm combines support vector machine and binary tree, it is a divisive (top-down) approach in which a set of classes is automatically separated into two smaller groups at each node of the hierarchy, it splits the classes based on the normalized cuts clustering algorithm. Our clustering algorithm considers the involved classes rather than the individual data samples. In the new proposed measures, similarity between classes is determined based on boundary complexity. In these measures, concepts such as the upper bound of error and Kolmogorov complexity are used. We reported results on several data sets and five distance/similarity measures. Experimental results demonstrate the superiority of the proposed measures compared to other measures; even when applied to nonlinearly separable data, the new criteria perform well.