A customer satisfaction degree evaluation model based on support vector machine

Authors:
Wang Ting;Hua Zhiwu
Affiliations:
Power University, Baoding, Hebei, China;Power University, Baoding, Hebei, China
Venue:
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
Year:
2008

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The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning

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Abstract

An efficient classification algorithm is proposed for evaluating the customer satisfaction degree. The algorithm is based on the RBF-Kernel support vector machine and multilevel binary tree classifier. Fuzzy membership function was used to quantify the evaluation indices. The evaluation indices and the SVM algorithm were used to design a customer satisfaction degree evaluation model. The novel evaluation method has higher accuracy in comparison with the traditional fuzzy comprehensive evaluation method and BP evaluation method.