| Project/Area Number |
24KF0254
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund |
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| Section | 外国 |
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| Review Section |
Basic Section 61040:Soft computing-related
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| Research Institution | Osaka Metropolitan University |
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Principal Investigator |
能島 裕介 大阪公立大学, 大学院情報学研究科, 教授 (10382235)
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| Co-Investigator(Kenkyū-buntansha) |
JIAO RUWANG 大阪公立大学, 大学院情報学研究科, 外国人特別研究員
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| Project Period (FY) |
2024-11-15 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2024)
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| Budget Amount *help |
¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2025: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2024: ¥500,000 (Direct Cost: ¥500,000)
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| Outline of Research at the Start |
This research is to investigate a multiform evolutionary framework that mainly addresses three issues of multi-objective feature selection in unbalanced classification scenarios: (1) the lack of recognition accuracy of minority class instances: We aim to utilize the advanced experience of selecting features on all classes to assist in the search for feature subsets. (2) the "curse of dimensionality": We aim to mitigate the adverse effects of dimensionality. (3) poor model interpretability: We seek to automatically construct interpretable classification models from carefully selected features.
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