A dynamic discretization approach for constructing decision trees with a continuous label

Hsiao Wei Hu, Yen Liang Chen, Kwei Tang

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.

Original languageEnglish
Article number4752827
Pages (from-to)1505-1514
Number of pages10
JournalIEEE Transactions on Knowledge and Data Engineering
Volume21
Issue number11
DOIs
StatePublished - Nov 2009

Keywords

  • Classification.
  • Data mining
  • Decision trees

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