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Issue No.11  November (2009 vol.21)
pp: 15051514
HsiaoWei Hu , National Central University, ChungLi
YenLiang Chen , National Central University, ChungLi
Kwei Tang , Purdue University, West Lafayette
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.24
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 nontreebased algorithms.
INDEX TERMS
Decision trees, data mining, classification.
CITATION
HsiaoWei Hu, YenLiang Chen, Kwei Tang, "A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label", IEEE Transactions on Knowledge & Data Engineering, vol.21, no. 11, pp. 15051514, November 2009, doi:10.1109/TKDE.2009.24
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