文献类型：期刊文献

英文题名：Multiple Partial Empirical Kernel Learning with Instance Weighting and Boundary Fitting

作者：Zhu, Zonghai[1,2];Wang, Zhe[1,2];Li, Dongdong[2];Du, Wenli[1];Zhou, Yangming[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：123

起止页码：26

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20195007811508);WOS:【SCI-EXPANDED(收录号:WOS:000511985000003)】；

基金：This work is supported by Natural Science Foundation of China under Grant No. 61672227, ``Shuguang Program'' supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, 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, and the Special Fund Project for Shanghai Informatization Development in Big Data under Grant 201901043.

语种：英文

外文关键词：Empirical Kernel Mapping; Multiple Empirical Kernel Learning; Instance weighting; Boundary fitting; Pattern recognition

摘要：By dividing the original data set into several sub-sets, Multiple Partial Empirical Kernel Learning (MPEKL) constructs multiple kernel matrixes corresponding to the sub-sets, and these kernel matrixes are decomposed to provide the explicit kernel functions. Then, the instances in the original data set are mapped into multiple kernel spaces, which provide better performance than single kernel space. It is known that the instances in different locations and distributions behave differently. Therefore, this paper defines the weight of instance in accordance with the location and distribution of the instances. According to the location, the instances can be categorized into intrinsic instances, boundary instances and noise instances. Generally, the boundary instances, as well as the minority instances in the imbalanced data set, are assigned high weight. Meanwhile, a regularization term, which regulates the classification hyperplane to fit the distribution trend of the class boundary, is constructed by the boundary instances. Then, the weight of instance and the regularization term are introduced into MPEKL to form an algorithm named Multiple Partial Empirical Kernel Learning with Instance Weighting and Boundary Fitting (IBMPEKL). Experiments demonstrate the good performance of IBMPEKL and validate the effectiveness of the instance weighting and boundary fitting. (C) 2019 Elsevier Ltd. All rights reserved.