A kernel fuzzy c-Means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises

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dc.contributor.author Yang, X
dc.contributor.author Zhang, G
dc.contributor.author Lu, J
dc.contributor.author Ma, J
dc.date.accessioned 2012-10-12T03:32:49Z
dc.date.issued 2011-02
dc.identifier.citation IEEE Transactions on Fuzzy Systems, 2011, 19 (1), pp. 105 - 115
dc.identifier.issn 1063-6706
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/17946
dc.description.abstract The support vector machine (SVM) has provided higher performance than traditional learning machines and has been widely applied in real-world classification problems and nonlinear function estimation problems. Unfortunately, the training process of the SVM is sensitive to the outliers or noises in the training set. In this paper, a common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises. In the KFCM-FSVM algorithm, we first use the FCM clustering to cluster each of two classes from the training set in the high-dimensional feature space. The farthest pair of clusters, where one cluster comes from the positive class and the other from the negative class, is then searched and forms one new training set with membership degrees. Finally, we adopt FSVM to induce the final classification results on this new training set. The computational complexity of the KFCM-FSVM algorithm is analyzed. A set of experiments is conducted on six benchmarking datasets and four artificial datasets for testing the generalization performance of the KFCM-FSVM algorithm. The results indicate that the KFCM-FSVM algorithm is robust for classification problems with outliers or noises. © 2006 IEEE.
dc.language eng
dc.relation.isbasedon 10.1109/TFUZZ.2010.2087382
dc.title A kernel fuzzy c-Means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises
dc.type Journal Article
dc.parent IEEE Transactions on Fuzzy Systems
dc.journal.volume 1
dc.journal.volume 19
dc.journal.number 1 en_US
dc.publocation USA en_US
dc.identifier.startpage 105 en_US
dc.identifier.endpage 115 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 0102 Applied Mathematics
dc.for 0906 Electrical and Electronic Engineering
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 001038
dc.personcode 020014
dc.personcode 999403
dc.percentage 34 en_US
dc.classification.name Applied Mathematics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Classification
dc.description.keywords fuzzy c-means (FCM)
dc.description.keywords fuzzy support vector machine (FSVM)
dc.description.keywords high-dimensional feature space
dc.description.keywords kernel clustering
dc.description.keywords outliers or noises
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history Closed (ID: 3)
utslib.collection.history Uncategorised (ID: 363)


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