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Least Squares Fuzzy One-class Support Vector Machine for Imbalanced Data
Jingjing Zhang,Kuaini Wang,Wenxin Zhu,Ping Zhong 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.8
Based on fuzzy one-class support vector machine (SVM) and least squares (LS) one-class SVM, we propose an LS fuzzy one-class SVM to deal with the class imbalanced problem. The LS fuzzy one-class SVM applies a fuzzy membership to each sample and attempts to solve the modified primal problem. Hence, we just need to solve a system of linear equations as opposed solving the quadratic programming problem (QPP) in fuzzy one-class SVM, which leads to an extremely simple and fast algorithm. Numerical experiments on several benchmark data sets demonstrate the feasibility and effectiveness of the proposed algorithm.
Privacy-Preserving One-Class Support Vector Machine with Vertically Partitioned Data
Qiang Lin,Huimin Pei,Kuaini Wang,Ping Zhong 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.5
We establish a new model of privacy-preserving one-class support vector machine (SVM) based on vertically partitioned data. Every participant holds all the data with a part of attributes. They apply different random matrices to establish their own kernel matrix. By sharing these partial kernel matrices, we construct a global kernel matrix and establish linear and nonlinear privacy-preserving models. Experimental results on benchmark data sets verify the validity of the proposed models.
Privacy-Preserving One-Class Support Vector Machine with Horizontally Partitioned Data
Qiang Lin,Huimin Pei,Kuaini Wang,Ping Zhong 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.9
We propose a new algorithm of privacy-preserving one-class support vector machine (SVM) with horizontally partitioned data. Every participant holds a part of data with all the data attributes. They apply the same random matrix to establish their own kernel matrix. By sharing these partial kernel matrices, we generate a global kernel matrix and establish two privacy-preserving one-class SVM models, which include the linear model and the nonlinear model. Partial kernel matrix can protect the privacy of the participants, and the global kernel matrix can ensure the classification accuracy. Experimental results on benchmark data sets indicate the effectiveness of the proposed algorithms.