关键词: 客户投诉分类, Random Subspace方法, 支持向量机, 证据推理规则

Abstract:

Customer complaints in the telecommunications industry are increasing and urgently need to be handled efficiently. Based on the characteristics of telecommunication customer complaint data, this paper proposes an improved ensemble learning classification method for high-dimensional data. The proposed method comprehensively considers the text information and the customer communication status information in the customer complaint, and then based on the Random Subspace, using the Support Vector Machine（SVM） as the base classifier, and using the Evidential Reasoning（ER） rule as a new ensemble strategy to construct classification model to classify the telecom customer complaints. The proposed model and method are validated on the customer complaint data of a telecommunication company. The experimental results show that the proposed method can significantly improve the accuracy of customer complaint classification and the efficiency of complaint handling.

Key words: customer complaint classification, Random Subspace, Support Vector Machine（SVM）, evidential reasoning rule