| Project/Area Number |
20700139
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Shimane University |
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Principal Investigator |
ABE Hidenao Shimane University, 医学部, 助教 (00397853)
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| Project Period (FY) |
2008 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
|
| Budget Amount *help |
¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2009: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2008: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | データマイニング / 機械学習 / ルール評価作業支援 / ルール評価指標 / メタ学習 / ルール評価支援 |
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| Research Abstract |
In this study, I assumed the iterative situation about if-then rule evaluations by human experts in post-processing phase of data mining. From the results of my empirical studies, we can get the following lessens-of -learn: (1)the initial cost to build rule evaluation models can be reduced, because the method only needs small balanced subsets of labeled rules sampled by using the accuracy of trial rule evaluation models. (2) At the initial rule evaluation iteration, a human expert can use some functional groups of objective rule evaluation indices to measure rules. And, the system can also build rule evaluation models by using these groups of indices. (3) The transitions of criteria of human experts can be detected by measuring rule evaluation models consisting of if-then rules.
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