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Evaluation of a FCM-based FSVM classifier using fuzzy index

Evaluation of a FCM-based FSVM classifier using fuzzy index Support vector machine (SVM), the new machine learning classification algorithm, has shown good generalation capability in binary classification problems. But, on datasets with outliers or noes, SVM has not shown good classification performance. As fuzzy support vector machine can significantly reduce the effect of outliers or noes, th study has adopted FSVM for model analys. As membership value on data points influence model performance, fuzzy C-means algorithm was used to evolve membership values on different fuzzy index values. With the new formulation of membership function, new membership values are created and used to run the FSVM model. The computational process of FSVM model on RBF kernel tested by grid search for different combinations of parameters and the performance of the model on different indices was observed. The classifier with highest classification accuracy for a particular index and kernel parameters identified as the best classifier for the dataset. Keywords: fuzzy support vector machine; FSVM; fuzzy C-means; FCM; membership; fuzzy index; kernel; classification accuracy. Reference to th paper should be made as follows: Punniyamoorthy, M. and Sridevi, P. (2017) `Evaluation of a FCM-based FSVM classifier using fuzzy index', Int. J. Enterpre Network Management, Vol. 8, No. 1, pp.14­34. Biographical notes: http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Enterprise Network Management Inderscience Publishers

Evaluation of a FCM-based FSVM classifier using fuzzy index

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Publisher
Inderscience Publishers
Copyright
Copyright © 2017 Inderscience Enterprises Ltd.
ISSN
1748-1252
eISSN
1748-1260
DOI
10.1504/IJENM.2017.083603
Publisher site
See Article on Publisher Site

Abstract

Support vector machine (SVM), the new machine learning classification algorithm, has shown good generalation capability in binary classification problems. But, on datasets with outliers or noes, SVM has not shown good classification performance. As fuzzy support vector machine can significantly reduce the effect of outliers or noes, th study has adopted FSVM for model analys. As membership value on data points influence model performance, fuzzy C-means algorithm was used to evolve membership values on different fuzzy index values. With the new formulation of membership function, new membership values are created and used to run the FSVM model. The computational process of FSVM model on RBF kernel tested by grid search for different combinations of parameters and the performance of the model on different indices was observed. The classifier with highest classification accuracy for a particular index and kernel parameters identified as the best classifier for the dataset. Keywords: fuzzy support vector machine; FSVM; fuzzy C-means; FCM; membership; fuzzy index; kernel; classification accuracy. Reference to th paper should be made as follows: Punniyamoorthy, M. and Sridevi, P. (2017) `Evaluation of a FCM-based FSVM classifier using fuzzy index', Int. J. Enterpre Network Management, Vol. 8, No. 1, pp.14­34. Biographical notes:

Journal

International Journal of Enterprise Network ManagementInderscience Publishers

Published: Jan 1, 2017

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