The modularity-based Hierarchical tree algorithm for multi-class classification
Chengwei Gu, Bofeng Zhang, Xinyue Wan, Mingqing Huang, Guobing Zou · 2016
Multi-class classification problem is still a research hotspot in machine learning, and researchers dedicate themselves to create new algorithms with higher efficiency and accuracy. A Modularity-based Hierarchical Classification Tree (MHCT) is proposed in this paper, which derived from the idea of community detection, and the structure of the tree is similar to the hierarchical cluster tree. This classification approach is a supervised learning method combined with modularity for its convergence indicator. After the building process of the tree from bottom to top, several classifier predictors are created in the training step. Finally, the comparison experiments are conducted between our methods and other two kinds of popular multi-class classification algorithms (i.e. support vector machine and decision tree), using ten benchmark datasets from UCI (University of California, Irvine) machine learning repository. In this work, the experimental results indicate that the proposed methods have drastically reduced the excessive training time while maintaining accuracy is comparable to the other algorithms.