Cluster Computing

, Volume 22, Supplement 5, pp 11965–11974 | Cite as

Identification of suitable membership and kernel function for FCM based FSVM classifier model

  • P. SrideviEmail author


Support vector machine (SVM) a new machine learning algorithm emerging as an effective classification technique than neural networks and other statistical classifiers. Its performance is however, observed to be affected by presence of outliers or noise in the dataset. So, to enhance the generalization capability of SVM, researchers focussed on developing fuzzy support vector machines (FSVM), which works on the concept of fuzzy membership to data points for learning the best decision hyperplane to classify data points. In such case, the choice of membership for data points is critical for good behaviour of the model. So, this paper focuses on finding right membership using optimal fuzzy index parameter (m) and demonstrating its significance in membership and classifier performance. This research work focussed on finding membership values using FCM and final membership from derived low value and high value membership function and incorporating them in FSVM model, to find the best membership value from analysis of five datasets used in the study. For linear classification of data, as kernel functions can show its influence, the study also performed analysis on selection of appropriate kernel function that can best fit the FCM based FSVM classifier model and prove enhancement of generalization performance with our research findings. High and low membership functions are compared through computational analyses and the membership that results in better generalization capability in the model is proven in the study.


Fuzzy support vector machine (FSVM) Fuzzy c-means (FCM) Fuzzy index Optimality Generalization performance Membership 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Management StudiesNational Institute of TechnologyTiruchirappalliIndia

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