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A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises

Journal Article


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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.

Authors


  •   Yang, Xiaowei (external author)
  •   Zhang, Guangquan (external author)
  •   Lu, Jie (external author)
  •   Ma, Jun

Publication Date


  • 2011

Citation


  • Yang, X., Zhang, G., Lu, J. & Ma, J. (2011). A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises. IEEE Transactions on Fuzzy Systems, 19 (1), 105-115.

Scopus Eid


  • 2-s2.0-79551627018

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7785&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6753

Has Global Citation Frequency


Number Of Pages


  • 10

Start Page


  • 105

End Page


  • 115

Volume


  • 19

Issue


  • 1

Place Of Publication


  • United States

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.

Authors


  •   Yang, Xiaowei (external author)
  •   Zhang, Guangquan (external author)
  •   Lu, Jie (external author)
  •   Ma, Jun

Publication Date


  • 2011

Citation


  • Yang, X., Zhang, G., Lu, J. & Ma, J. (2011). A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises. IEEE Transactions on Fuzzy Systems, 19 (1), 105-115.

Scopus Eid


  • 2-s2.0-79551627018

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7785&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6753

Has Global Citation Frequency


Number Of Pages


  • 10

Start Page


  • 105

End Page


  • 115

Volume


  • 19

Issue


  • 1

Place Of Publication


  • United States