Data Mining from Large High-dimensional Data by Multiobjective Genetics-based Machine Learning
| Project/Area Number |
16K00336
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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 |
Soft computing
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
Nojima Yusuke 大阪府立大学, 工学(系)研究科(研究院), 准教授 (10382235)
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| Project Period (FY) |
2016-04-01 – 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: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Keywords | 知識獲得 / 多目的最適化 / 並列分散実装 / 遺伝的アルゴリズム / 大規模多属性データ |
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| Outline of Final Research Achievements |
In recent years, data science is one of the hottest topics in computer science. Various data mining techniques have been developed so far. Among them, our multiobjective genetics-based machine learning is a data mining technique which can generate a number of knowledge models with different accuracy and complexity by its single run. In this research, we improved its effectiveness for large and high-dimensional data sets from both algorithmic and implementation points of view. We also extend our multiobjective genetics-based machine learning for the analysis on mechanical design problems, image data classification problems and multi-label classification problems.
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| Academic Significance and Societal Importance of the Research Achievements |
大規模多属性データからの知識獲得において，計算時間の短縮と得られた知識の分かりやすさは非常に重要である．本研究では，知識の精度と分かりやすさを同時に最適化する多目的遺伝的機械学習手法の高効率化を行った．また，これまでの数値データからの知識獲得だけでなく，様々な応用問題へと展開したことで，今後，解釈可能なAIの発展に寄与できると考えられる．
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Report
(4 results)
Research Products
(14 results)
