Decision tree induction with a constrained number of leaf nodes

Chia Chi Wu, Yen Liang Chen, Yi Hung Liu, Xiang Yu Yang

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish
Pages (from-to)673-685
Number of pages13
JournalApplied Intelligence
Issue number3
StatePublished - 1 Oct 2016


  • Classification
  • Constraint tree
  • Data mining
  • Decision tree


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