TREE CLASSIFIER IN SINGULAR VERTOR SPACE

摘要：

This paper proposes a tree classifier in the local singular vector space of data, named LST algorithm. LST builds oblique decision trees by first transforming local data on the internal nodes to the orthogonal singular vector space and then construting univariant decision tree nodes in the new space. LST can handle datasets with totally different local and global distribution. Theoretical analysis proves that the time complexity of LST is the same as that of the univariant decision tree algorithms, besides the classification result of LST will not be affected by the arrangement of data samples. Experimental results also show that, compared with the state-of-art univariant decision tree algorithm C4.S and the well known oblique decision tree algorithms OC1 and CART-LC, LST produces higher classification accuracy, more stable decision tree size, comparable tree construction time as C4.5 and much less than OCl and CAKT-LC.

关键词： Tree Classifier Singular Vector Space Oblique Decision Tree

作者: PING HE XIAO-HUA XU LING CHEN

作者单位: Department of Computer Science, Yangzhou University, Yangzhou 225009, China Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics,N

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1801-1806

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）