Sample-Efficient Regression Trees for Attributes with Mixed Continuous and Discrete Effects-A Piecewise-Linear Regression Tree
Date Issued
2005
Date
2005
Author(s)
Huang, Yi-Hsi
DOI
en-US
Abstract
Classification and regression trees (CART) is a type of decision-tree techniques, used to deal with either categorical or continuous response. A shortcoming of the regression tree is that the splitting procedure exhausts the sample size quickly. Sample-Efficient Regression Trees (SERT) is developed to address the sample-size-depleting issue. However, both SERT and CART are only able to select the attributes with discrete effects. The attributes with continuous effects, variant continuous effects, and mixed effects will not be selected into the tree model by CART and SERT.
In this research, we integrate the stepwise regression method and sample-efficient regression tree approach to select attributes with continuous effects. When dealing with attributes with variant continuous effects, we propose a method to consider simultaneously the continuous effect and discrete effect of an attribute. For the attributes with mixed effects, we consider not only the effect of attribute but also that of the attributes selected subsequently.
In order to validate the methods we proposed, we test the proposed tree using some simulated data with continuous effects, variant effects, and mixed effects. A real case about the body density of 252 men is also studied. With the validation of the simulated data and the real case, we verify that the new decision tree is able to select attributes that other decision trees fail to select and build a more robust tree model with attributes effects more accurately estimated.
Subjects
迴歸樹
regression trees
Sample-Efficient Regression Trees
Type
thesis
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