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Activity Number: 552
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #314853
Title: An Approach for Constructing Regression Tree on Interval-Valued Variables
Author(s): Asanao Shimokawa* and Yohei Kawasaki and Etsuo Miyaoka
Companies: Tokyo University of Science and University of Shizuoka and Tokyo University of Science
Keywords: binarry tree ; CART ; interval-valued variable ; symbolic data
Abstract:

The regression tree, which is constructed using the CART algorithm, is considered in this study. In our model, both response and explanatory variables can be assumed as interval-valued symbolic variables. The proposed model allows that a concept is included in several terminal nodes in a tree-structured model. Then, the prediction of a new concept will be constructed by using all terminal nodes that includes the concept. If we want to construct the proposed model, several problems such as the representation method of predictive models in each node and searching an optimal splitting point in interval values, should be addressed. We address these problems and present an application of this model. From the comparison to the analysis based on the classical regression tree approach, we can obtain another aspect of the data by the proposed approach.


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