文献类型：期刊文献

英文题名：Multiple Universum Empirical Kernel Learning

作者：Wang, Zhe[1,2];Hong, Sisi[2];Yao, Lijuan[2];Li, Dongdong[2];Du, Wenli[1];Zhang, Jing[2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：89

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20200107982414);WOS:【SCI-EXPANDED(收录号:WOS:000515429100023)】；

基金：This work is supported by Natural Science Foundation of China under Grant No. 61672227, "Shuguang Program" supported by Shanghai Education Development Foundation, PR China and Shanghai Municipal Education Commission, PR China, Natural Science Foundations of China under Grant No. 61806078, National Science Foundation of China for Distinguished Young Scholars under Grant 61725301, National Major Scientific and Technological Special Project for "Significant New Drugs Development" under Grant No. 2019ZX09201004, the Special Fund Project for Shanghai Informatization Development in Big Data under Grant 201901043, and National Key R&D Program of China under Grant No. 2018YFC0910500.

语种：英文

外文关键词：Multiple kernel learning; Empirical kernel mapping; Universum learning; Imbalanced data; Pattern recognition

摘要：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.