Title :
A Customer Satisfaction Degree Evaluation Model Based on Support Vector Machine
Author :
Ting, Wang ; Zhiwu, Hua
Author_Institution :
North China Electr. Power Univ., Baoding
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; radial basis function networks; support vector machines; tree data structures; RBF-Kernel support vector machine; classification algorithm; customer satisfaction degree evaluation model; fuzzy membership function; multilevel binary tree classifier; Classification algorithms; Customer satisfaction; Data mining; Energy management; Kernel; Knowledge management; Risk management; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Knowledge Discovery and Data Mining, 2008. WKDD 2008. First International Workshop on
Conference_Location :
Adelaide, SA
Print_ISBN :
978-0-7695-3090-1
DOI :
10.1109/WKDD.2008.24
