Explanatory Analysis of Probabilistic Graphical Models based on Discriminative Pattern Mining
| Project/Area Number |
24700141
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent informatics
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| Research Institution | Meijo University |
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Principal Investigator |
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| Project Period (FY) |
2012-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2012: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | ベイジアンネット / 説明的分析 / 識別パターン発見 / ベイジアンネットワーク / 識別パターン / 分枝限定法 |
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| Research Abstract |
In this project, we have developed an explanatory analysis method for probabilistic models, Bayesian networks in paticular, to bring explanability to machine learning techniques. Our analysis aims to find an appropriate explanation for the observation from a huge number of possible ones. To do this in practical time, we built some sophisticated techniques for discriminative pattern mining based on a popular frequent pattern mining algorithm called FP-Growth. Finally, we have achieved to refine the selection criteria of explanations and to have a fast discriminative pattern mining algorithm. Although there remains a future work on optimizing probabilistic inference for our explanatory analysis, we have obtained a couple of new insights and prototype tools towards an implementation of our explanatory analysis method.
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Report
(3 results)
Research Products
(6 results)
