Presentation 1997/12/2
Efficient Construction of Regression Trees with Range and Region Splitting
Hiromu Ishii, Yasuhiko Morimoto, Shinichi Morishita,
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Abstract(in English) In recent years data mining has attracted many researchers among both artificial intelligence and database communities. Construction of regression trees is a topic of data mining. A regression tree is a rooted binary tree such that each internal node contains a test for splitting tuples into two disjoint classes. The mean of the objective attribute values at the leaf is used as the predicted value of the tuple. To test a numerical attribute, traditional methods use a guillotine-cut splitting that classifies data into those below a given value and others. In this paper, as an alternative of guillotine-cut splitting, we consider a family R of grid-regions in the plane associated with two given numeric attributes. And we propose to use a test that splits data into those that lie inside a region R and those that lie outside. Some experimental results showed that regression trees constructed through our method have higher accuracy than those through guillotine-cut splitting.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) data mining / numerical value prediction / regression tree / range and region splitting / convex function
Paper # AI97-43
Date of Issue

Conference Information
Committee AI
Conference Date 1997/12/2(1days)
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Paper Information
Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Efficient Construction of Regression Trees with Range and Region Splitting
Sub Title (in English)
Keyword(1) data mining
Keyword(2) numerical value prediction
Keyword(3) regression tree
Keyword(4) range and region splitting
Keyword(5) convex function
1st Author's Name Hiromu Ishii
1st Author's Affiliation Department of Information Science, University of Tokyo()
2nd Author's Name Yasuhiko Morimoto
2nd Author's Affiliation Tokyo Research Laboratory, IBM Japan Ltd.
3rd Author's Name Shinichi Morishita
3rd Author's Affiliation Institute of Medical Science, University of Tokyo
Date 1997/12/2
Paper # AI97-43
Volume (vol) vol.97
Number (no) 415
Page pp.pp.-
#Pages 6
Date of Issue