NDLI: A SVM multi-classification method based on fuzzy weighted SVDD

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2010 2nd International Conference on Software Technology and Engineering

Volume 2 - 2010

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Volume 1 - 2010

A SVM multi-classification method based on fuzzy weighted SVDD

Content Provider	IEEE Xplore Digital Library
Author	Shi Xiao-yun Yu Tie Chen Quan
Copyright Year	2010
Description	Author affiliation: Chaozhou Fire Detachment, China (Yu Tie) \|\| Tianjin University of Technology, China (Shi Xiao-yun) \|\| School of Environmental Science & Safety Engineering, Tianjin University of Technology, China (Chen Quan)
Abstract	Aiming at sensitivity of noise on WSVDD and circumstance of can not be separated in multi-classification problem, the paper presented a multi-classification method based on fuzzy weighted support vector description algorithm. Inspired by weighed SVDD, the method assigned weight to each training sample to build super-ball, while its weight does not take into account the effect of characteristics of sample data itself and noise. Noise fuzzy kernel clustering method was used to determine membership degree of samples and fuzzy weighted SVDD model was built. The multi-classification algorithm and simple classification rules were also provided. The example proves that the method can effectively reduce the effect of noise on classifier, and it can also achieve relatively better training accuracy.
File Size	660963
File Format	PDF
ISBN	9781424486670
e-ISBN	9781424486663
DOI	10.1109/ICSTE.2010.5608761
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Multi-classification method Fuzzy clustering Noise Support vector data description Clustering algorithms Classification algorithms Kernel Optimization
Content Type	Text
Resource Type	Article

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