2000 Fiscal Year Final Research Report Summary
Data Mining from Large Data Set by Generalized Tree Regression Model
| Project/Area Number |
11680437
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
社会システム工学
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| Research Institution | Gunma University |
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Principal Investigator |
SEKI Yoichi Gunma University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (90196949)
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| Project Period (FY) |
1999 – 2000
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| Keywords | Tree regression model / Data mining / Minimum Description Length / Interaction effect / Linear regression / Poisson compound distribution / Care needs certification / Long-term care insurance |
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| Research Abstract |
Our research project dealt with tree regression models as data mining method. We studied the below subjects as generalization of the ordinal tree regression model.
(a) Generalization of the covariates use as additive term
We propose the method to estimate tree models which have linear regression terms in each node of regression tree, using degree of interaction effect as splitting criterion. The method enable us to make a simple tree which have fewer splits than ordinal tree regression models.
(b) Generalization of distribution assumption of response variable
Ordinal tree regression model assume implicitly that response variable distributes as normal distribution. To apply the models to the service time response data, we propose the method under the assumption that response variable has Poisson compound exponential distribution.
(c) Varidity study of the tree regression model
We analyze empirical data to confirm the varidity of proposed methods, for example, Long-term care service time data which was investigated for the Care Needs Certification in Japanese Long-term Care Insurance.
The reduction of calculation time remains as future study.
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