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Class Balancing in Customer Segments Classification Using Support Vector Machine Rule Extraction and Ensemble Learning

Suncica Rogic¹ and Ljiljana Kascelan¹

1. University of Montenegro
   81000 Podgorica, Montenegro
   suncica@ucg.ac.me, ljiljak@ucg.ac.me

Abstract

An objective and data-based market segmentation is a precondition for efficient targeting in direct marketing campaigns. The role of customer segments classification in direct marketing is to predict the segment of most valuable customers who is likely to respond to a campaign based on previous purchasing behavior. A good-performing predictive model can significantly increase revenue, but also, reduce unnecessary marketing campaign costs. As this segment of customers is generally the smallest, most classification methods lead to misclassification of the minor class. To overcome this problem, this paper proposes a class balancing approach based on Support Vector Machine-Rule Extraction (SVM-RE) and ensemble learning. Additionally, this approach allows for rule extraction, which can describe and explain different customer segments. Using a customer base from a company’s direct marketing campaigns, the proposed approach is compared to other data balancing methods in terms of overall prediction accuracy, recall and precision for the minor class, as well as profitability of the campaign. It was found that the method performs better than other compared class balancing methods in terms of all mentioned criteria. Finally, the results confirm the superiority of the ensemble SVM method as a preprocessor, which effectively balances data in the process of customer segments classification.

Key words

direct marketing, customer classification, class imbalance, SVM-Rule Extraction, ensemble

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200530052R

Publication information

Volume 18, Issue 3 (June 2021)
Year of Publication: 2021
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

Available in PDF
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How to cite

Rogic, S., Kascelan, L.: Class Balancing in Customer Segments Classification Using Support Vector Machine Rule Extraction and Ensemble Learning. Computer Science and Information Systems, Vol. 18, No. 3, 893–925. (2021), https://doi.org/10.2298/CSIS200530052R