文献类型：期刊文献

英文题名：Multi-kernel Support Vector Data Description with boundary information

作者：Guo, Wei[1,2];Wang, Zhe[1,2];Hong, Sisi[1,2];Li, Dongdong[2];Yang, Hai[2];Du, Wen[3]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[3]DS Informat Technol Co Ltd, Shanghai 200032, Peoples R China

年份：2021

卷号：102

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20211910317922);WOS:【SCI-EXPANDED(收录号:WOS:000663472500018)】；

基金：This work is supported by Natural Science Foundation of China under Grant No. 62076094, Shanghai Science and Technology Program, China "Distributed and generative fewshot algorithm and theory research"under Grant No. 20511100600, Key Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security) , China under Grant No. C20603, Natural Science Foundation of China under Grant No. 61806078, National Key Research and Development Project of Ministry of Science and Technology of China under Grant No. 2018AAA0101302, National Major Scientific and Technological Special Project, China for "Significant New Drugs Development'' under Grant No. 2019ZX09201004, Zhejiang Lab under Grant No. 2019ND0AB01, and National Science Foundation of China for Distinguished Young Scholars under Grant No. 61725301.

语种：英文

外文关键词：One-Class Classification; Multiple Kernel Learning; Boundary information; Kernel weights; Pattern recognition

摘要：The One-Class Classification (OCC) exists in many real-world applications, such as novelty detection, outlier detection, facial verification, and anomaly detection. SVDD is an efficient method to solve the OCC problem. How to describe a hypersphere with minimized volume that encloses almost all the target class samples is the key point of the SVDD. However, the existing SVDD-based methods generally neglect the boundary information of samples in the construction of the classifier. To fully utilize the boundary information to guide the training process, this paper introduces Multi-Kernel Learning (MKL) into the traditional Support Vector Data Description (SVDD) based on the boundary information, proposes a novel method called MKLSVDD. The proposed MKL-SVDD first determines the boundary samples based on the geometrical and statistical information, and assigns the special weight for the boundary samples to improve the effect of the boundary samples in the optimization process. Meanwhile, to enhance the feature expression capabilities, the proposed MKL-SVDD utilizes the location information of samples as the supervised signal to design the kernel weights for multiple kernels to obtain the optimal kernel combination. Extensive experiments on 11 UCI datasets and 7 KEEL datasets demonstrate the superiority of the MKL-SVDD over the other state-of-the-art methods. The Bayesian analysis of the experiment results theoretically prove that the MKL-SVDD is superior to other methods on UCI and KEEL datasets with 100% and over 96% probability respectively.