2007 Fiscal Year Final Research Report Summary
Accurate methods for incomplete data analysis using the mixed trunsored model and the decision tree.
| Project/Area Number |
17510127
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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 |
Social systems engineering/Safety system
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
HIROSE Hideo Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineering, Professor (60275401)
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| Project Period (FY) |
2005 – 2007
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| Keywords | mixed trunsored model / decision tree / bump hunting / genetic algorithm / extreme-vale distribution / power law / classification / epidemiology |
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| Research Abstract |
The objective of this project is to analyze the incomplete data cases where the data are consist of inhomogeneous data by estimating the model parameters and testing the hypotheses. To do this, first we tried to combine the data mining scheme with the incomplete data analysis methods.
We have shown that the mixed trunsored model has applicability to many fields such as the medical area or the reliability fields. The fact that the paper of IEEE Transaction was awarded by Japanese IEEE Reliability Society shows that the research level is highly evaluated.
Also, the bump hunting method, which is useful to classify the messy data, has been shown with algorithms, and the numerical examples in marketing have been illustrated. We have had many presentations in IEEE, IFORS, and HICSS etc.
The combined method of the trunsored model and the decision tree model has been developed with a concrete example in electrical insulation applications.
Thus, we have reached the goal we posed successfully.
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Research Products
(34 results)
