A hierarchical cluster validity based visual tree learning for hierarchical classification

Abstract

For hierarchical learning, one open issue is how to build a reasonable hierarchical structure which characterize the inter-relation between categories. An effective approach is to utilize hierarchical clustering to build a visual tree structure, however, the critical issue of this approach is how to determine the number of clusters in hierarchical clustering. In this paper, a hierarchical cluster validity index (HCVI) is developed for supporting visual tree learning. Before clustering of each level begins, we will measure the impact of different numbers of clusters on visual tree building and select the most suitable number of clusters. The proposed HCVI will control the structure of visual tree neither too flat nor too deep. Based on this visual tree, a hierarchical classifier can be trained for achieving more discriminative capability. Our experimental results have demonstrated that the proposed hierarchical cluster validity index (HCVI) can guide the building of a more reasonable visual tree structure, so that the hierarchical classifier can achieve better results on classification accuracy.