A Customer Satisfaction Degree Evaluation Model Based on Support Vector Machine

Wang Ting; Hua Zhiwu

1st International ICST Workshop on Knowledge Discovery and Data Mining

Research Article

A Customer Satisfaction Degree Evaluation Model Based on Support Vector Machine

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/wkdd.2008.2791,
    author={Wang Ting and Hua Zhiwu},
    title={A Customer Satisfaction Degree Evaluation Model Based on Support Vector Machine},
    proceedings={1st International ICST Workshop on Knowledge Discovery and Data Mining},
    publisher={ACM},
    proceedings_a={WKDD},
    year={2010},
    month={5},
    keywords={customer satisfaction degree support vector machine multilevel binary tree classifier evaluation model},
    doi={10.4108/wkdd.2008.2791}
}

Wang Ting
Hua Zhiwu
Year: 2010
A Customer Satisfaction Degree Evaluation Model Based on Support Vector Machine
WKDD
ACM
DOI: 10.4108/wkdd.2008.2791

Wang Ting¹^,*, Hua Zhiwu¹

1: Department of Economy Management, North China Electric Power University, Baoding, Hebei, China.

*Contact email: abcde-20015@163.com

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.

Keywords: customer satisfaction degree support vector machine multilevel binary tree classifier evaluation model

Published: 2010-05-16
Publisher: ACM
Modified: 2010-05-16

: http://dx.doi.org/10.4108/wkdd.2008.2791