| Project/Area Number |
26540116
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent informatics
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| Research Institution | Osaka University |
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Principal Investigator |
Washio Takashi 大阪大学, 産業科学研究所, 教授 (00192815)
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| Co-Investigator(Renkei-kenkyūsha) |
SHIMIZU Shohei 大阪大学, 産業科学研究所, 准教授 (10509871)
KAWAHARA Yoshinobu 大阪大学, 産業科学研究所, 准教授 (00514796)
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| Project Period (FY) |
2014-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | データマイニング / 機械学習 / ビッグデータ / モデリング / 高次元データ / サンプリング / アンサンブル / アンサンブル学習 / 心疾患モデル / 列挙探索 / 超高次元データ / 大規模データ |
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| Outline of Final Research Achievements |
This study aimed at the exploration of model mining principles, which enable fast search of candidate models representing sub-processes embedded in super-high dimensional and large scale data, and their implementations into some algorithms for applying to experimental problems including medical fields. We established novel principles of random sub-sampling and ensemble modeling for fast and accurate model mining from the large scale data, and developed the methods of half-space mass and and mass based similarity measures by implementing the principles. Finally, by applying these methods to heart disease data in medicine, we succeeded to mine a model of a occurrence mechanism of the heart disease. These outcomes have been presented in Machine Learning:the world top journal of machine learning, ICDM: the world top international of data mining and a major medical journal.
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