Fu Saiji
讲师（高校）
Gender:
Female
Alma Mater:
中国科学院大学
Education Level:
研究生毕业
Degree:
博士学位
Status:
在职
School/Department:
经济管理学院
Discipline:
Management science and engineering *
E-Mail:
3743bbe3fd14aaa444fa0277779fdb3d658b74b96bad78b74a5c62d2d1c9f23ff9b6a820ab83c8c8304b39c0fd126c3feea95a8ac3405d1be5c0cdf005f96d7d5f7c639fab47dbf2bdbe3c0943d0b436e368f7bc2300626c95dd05d18e9059181af59cbce41e9e3213911acb703acb51a0088238bf8237ddbfd8e5c1c25442e8
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Paper Publications
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[1] Hou Z, Tang J, Li Y, Fu S, et al. MVQS: Robust multi-view instance-level cost-sensitive learning method for imbalanced data classification[J]. Information Sciences, 2024: 120467. （SCI，中科院一区TOP）
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[2] Tang J, Yi Q, Fu S*, et al. Incomplete multi-view learning: Review, analysis, and prospects[J]. Applied Soft Computing, 2024: 111278.（SCI, 中科院一区TOP）
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[3] Liu D, Fu S, Tian Y, et al. Universum driven cost-sensitive learning method with asymmetric loss function[J]. Engineering Applications of Artificial Intelligence, 2024, 131: 107849.（SCI, 中科院二区TOP）
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[4] Fu S, Wang X, Tang J, et al. Generalized robust loss functions for machine learning[J]. Neural Networks, 2024, 171: 200-214.（SCI, 中科院一区TOP）
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[5] Fu S, Wang X, Tian Y, et al. Coarse-grained privileged learning for classification[J]. Information Processing & Management, 2023, 60(6): 103506. （SCI, 中科院一区TOP）
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[6] Fu S, Su D, Li S, et al. Linear-exponential loss incorporated deep learning for imbalanced classification[J]. ISA Transactions, 2023, 140: 279-292. （SCI, 中科院二区TOP）
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[7] Tang J, Hou Z, Yu X, Tian Y, Fu S*. Multi-view cost-sensitive kernel learning for imbalanced classification problem[J].Neurocomputing, 2023, 552: 126562.（SCI, 中科院二区TOP）
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[8] Fu S, Tian Y, Tang J. Iterative privileged learning for multi-view classification[C]// International Conference on Information Technology and Quantitative Management, 2023. （EI）
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[9] Fu S, Tian Y, Tang L. Robust regression under the general framework of bounded loss functions[J]. European Journal of Operational Research, 2023, 310(3): 1325-1339. （ABS 4）
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[10] Tian Y, Zhao X, Fu S*. Kernel methods with asymmetric and robust loss function[J]. Expert Systems with Applications, 2023, 213: 119236.（SCI, 中科院一区TOP）
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