| Project/Area Number |
15K00312
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Hiroshima City University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
内田 智之 広島市立大学, 情報科学研究科, 准教授 (70264934)
久保山 哲二 学習院大学, 計算機センター, 教授 (80302660)
廣渡 栄寿 北九州市立大学, 基盤教育センター, 教授 (60274429)
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | データマイニング / 機械学習 / グラフ構造データ / 木構造データ / 遺伝的プログラミング / グラフ構造パターン / 木構造パターン / 進化的学習 |
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| Outline of Final Research Achievements |
We have studied discovery of deep knowledge from graph-structured data using expressive graph-structured patterns. We have proposed a machine learning method for acquiring characteristic block preserving outerplanar graph patterns from positive and negative outerplanar graph data. We have proposed machine learning methods for acquiring characteristic multiple blockblock preserving outerplanar graph patterns and characteristic multiple TTSP graph patterns from positive and negative graph data.These methods are based on Genetic Programming, which is an evolutionary learning method dealing with tree structures. We have proposed other learning methods for discovering knowledge from graph-structured data.
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| Academic Significance and Societal Importance of the Research Achievements |
グラフ構造データから知識を発見して活用するためのデータマイニングと機械学習における基盤技術を確立するために，従来手法では表現できなかったパターン型深層知識を発見する手法を開発した．
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