NDLI: The support vector machine based on intuitionistic fuzzy number and kernel function

Content Provider	Springer Nature Link
Author	Ha, Minghu Wang, Chao Chen, Jiqiang
Copyright Year	2012
Abstract	Fuzzy support vector machine applied a degree of membership to each training point and reformulated the traditional support vector machines, which reduced the effects of noises and outliers for classification. However, the degree of membership only considered the distance from samples to the class center in the sample space, while neglected the situation of samples in the feature space and easily mistook the edge support vectors as noises. To deal with the aforementioned problems, the support vector machine based on intuitionistic fuzzy number and kernel function is proposed. In the high-dimensional feature space, each training point is assigned with a corresponding intuitionistic fuzzy number by the use of kernel function. Then, a new score function of the intuitionistic fuzzy numbers is introduced to measure the contribution of each training point. In the end, the new support vector machine is constructed according to the score value of each training point. The simulation results demonstrate the effectiveness and superiority of the proposed method.
Starting Page	635
Ending Page	641
Page Count	7
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	17
Issue Number	4
e-ISSN	14337479
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-09-21
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Support vector machine Intuitionistic fuzzy number Score function Kernel function Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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