期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 89, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2019.103461
关键词
Multiple kernel learning; Empirical kernel mapping; Universum learning; Imbalanced data; Pattern recognition
类别
资金
- Natural Science Foundation of China [61672227]
- Shuguang Program - Shanghai Education Development Foundation, PR China
- Shanghai Municipal Education Commission, PR China
- Natural Science Foundations of China [61806078]
- National Science Foundation of China for Distinguished Young Scholars [61725301]
- National Major Scientific and Technological Special Project for Significant New Drugs Development [2019ZX09201004]
- Special Fund Project for Shanghai Informatization Development in Big Data [201901043]
- National Key R&D Program of China [2018YFC0910500]
This paper proposes a novel framework called Multiple Universum Empirical Kernel Learning (MUEKL) that combines the Universum learning with Multiple Empirical Kernel Learning (MEKL) for the first time to inherit the advantages of both techniques. The proposed MUEKL not only obtained supplementary information of multiple feature spaces through MEKL, but also obtained priori information of samples by Universum learning. MUEKL incorporates a novel method, Imbalanced Modified Universum (IMU), to generate more efficient Universum samples by introducing the imbalanced ratio of data. MUEKL develops the basic multiple kernel learning framework by introducing a regularization of Universum data. The function of the introduced regularization is to adjust the classifier boundary closer to the Universum data to alleviate the influence of the imbalanced data. Moreover, MUEKL performs excellent generalization for both the imbalanced and balanced problems. Extensive experiments verify the effectiveness of the MUEKL and IMU.
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