Decision tree induction with a constrained number of leaf nodes

详细信息    查看全文

• 作者：Chia-Chi Wu ; Yen-Liang Chen ; Yi-Hung Liu ; Xiang-Yu Yang
• 关键词：Classification ; Data mining ; Decision tree ; Constraint tree
• 刊名：Applied Intelligence
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：45
• 期：3
• 页码：673-685
• 全文大小：1,178 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497
• 卷排序：45

文摘

With the advantages of being easy to understand and efficient to compute, the decision tree method has long been one of the most popular classifiers. Decision trees constructed with existing approaches, however, tend to be huge and complex, and consequently are difficult to use in practical applications. In this study, we deal with the problem of tree complexity by allowing users to specify the number of leaf nodes, and then construct a decision tree that allows maximum classification accuracy with the given number of leaf nodes. A new algorithm, the Size Constrained Decision Tree (SCDT), is proposed with which to construct a decision tree, paying close attention on how to efficiently use the limited number of leaf nodes. Experimental results show that the SCDT method can successfully generate a simpler decision tree and offers better accuracy.