A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29641

Title:	A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label
Authors:	Hu,HW;Chen,YL;Tang,K
Contributors:	資訊管理研究所
Keywords:	SELECTION;REGRESSION;ALGORITHM
Date:	2009
Issue Date:	2010-06-29 20:37:08 (UTC+8)
Publisher:	中央大學
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.
Relation:	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Appears in Collections:	[Graduate Institute of Information Management] journal & Dissertation

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