Study on Data Mining by Hybrid Modeling
| Project/Area Number |
13680536
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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 | Meiji University |
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Principal Investigator |
OOTAKI Atsushi Meiji University, Mechanical Engineering Informatics, School of Science and Technology, Professor, 理工学部, 教授 (20061971)
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| Project Period (FY) |
2001 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 2002: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2001: ¥700,000 (Direct Cost: ¥700,000)
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| Keywords | CART / MARS / AR model / missing data / residual analysis / 自由回答形式アンケート / WEBアンケート調査 / 形態素解析 / 数量化III類 / 商品企画 / データマイニング / ハイブリッド / 欠測値処理 |
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| Research Abstract |
To mine data set for an optimal model taking into account both linear and non-linear parts of variation of response variable, a strategy of hybrid modeling for combining nonparametric statistical model such as CART/MARS with linear regression analysis is proposed after the features are extracted from datasets. To valid the strategy, the datasets are applied, and useful results are obtained as follows.
(1) Hybrid modeling can improve accuracy of the sole modeling of CART and/or regression model.
(2) It can handle any case with surrogate variable instead of any predictor, which has a missing value.
(3) Autocorrelation of residual time series and its residual mean squared error after hybrid can be improved by application of autoregressive model.
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Report
(3 results)
Research Products
(7 results)
