For classification problems of customer relationship management (CRM), the purpose of this paper is to propose a method with interpretability of the classification results that combines multiple decision trees based on a genetic algorithm.
In the proposed method, multiple decision trees are combined in parallel. Subsequently, a genetic algorithm is used to optimize the weight matrix in the combination algorithm.
The method is applied to customer credit rating assessment and customer response behavior pattern recognition. The results demonstrate that compared to a single decision tree, the proposed combination method improves the predictive accuracy and optimizes the classification rules, while maintaining interpretability of the classification results.
The findings of this study contribute to research methodologies in CRM. It specifically focuses on a new method with interpretability by combining multiple decision trees based on genetic algorithms for customer classification.
The authors sincerely thank seminar participants at Center for Data-Driven Managerial Decision Making. This work is supported by the National Natural Science Foundation of China (Grant Nos 71672038 and 71222104) and The China Scholarship Council (Grant No. 201706105007).
Zhang, Z. and Dai, Y. (2019), "Combination classification method for customer relationship management", Asia Pacific Journal of Marketing and Logistics, Vol. 32 No. 5, pp. 1004-1022. https://doi.org/10.1108/APJML-03-2019-0125Download as .RIS
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