Multiple Universum Empirical Kernel Learning. (March 2020)

• Record Type: Journal Article
• Authors: Wang, Zhe; Hong, Sisi; Yao, Lijuan; Li, Dongdong; Du, Wenli; Zhang, Jing
• Is Part Of: Engineering applications of artificial intelligence. Volume 89(2020)
• Publication Date: 2020-03
• Subjects: Multiple kernel learning -- Empirical kernel mapping -- Universum learning -- Imbalanced data -- Pattern recognition; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.103461
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.