A hierarchical clustering and fixed-layer local learning based support vector machine algorithm for large scale classification problems

作者：Wu, Guang-Chao^*; Xiao, Fa-Zhen; Xi, Jian-Qing; Yang, Xiao-Wei; He, Li-Fang; Lü, Hao-Ran; Liu, Xiao-Lan

来源：Journal of Dong Hua University (english Edition), 2012, 29(1): 46-50.

• Clustering feature
• Data sets
• Hier-archical clustering
• Local learning
• Subtrees
• Supervised clustering
• Support vector machine algorithm
• Testing accuracy
• Training and testing
• Unsupervised clustering
• Vector machines

It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed-layer local learning (HCFLL) based support vector machine (SVM) algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically clusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision-tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.

• 出版日期2012-2
• 单位华南理工大学