Abstract
Customer churn prediction is one of the challenging problems and paramount concerns for telecommunication industries. With the increasing number of mobile operators, users can switch from one mobile operator to another if they are unsatisfied with the service. Marketing literature states that it costs 5–10 times more to acquire a new customer than retain an existing one. Hence, effective customer churn management has become a crucial demand for mobile communication operators. Researchers have proposed several classifiers and boosting methods to control customer churn rate, including deep learning (DL) algorithms. However, conventional classification algorithms follow an error-based framework that focuses on improving the classifier's accuracy over cost sensitization. Typical classification algorithms treat misclassification errors equally, which is not applicable in practice. On the contrary, DL algorithms are computationally expensive as well as time-consuming. In this paper, a novel class-dependent cost-sensitive boosting algorithm called AdaBoostWithCost is proposed to reduce the churn cost. This study demonstrates the empirical evaluation of the proposed AdaBoostWithCost algorithm, which consistently outperforms the discrete AdaBoost algorithm concerning telecom churn prediction. The key focus of the AdaBoostWithCost classifier is to reduce false-negative error and the misclassification cost more significantly than the AdaBoost.
Highlights
In developing countries, smartphones play a significant role in human life, and the number of mobile operators is rapidly increasing in every technologically advanced country
In this fierce competitive nature of the wireless telecommunication industry, customers have unlimited freedom to migrate from one service provider to another. is phenomenon is known as churn
Experimental Method. e investigations of the study estimate the three measure indices for telecom churn prediction which have utmost importance, the false-negative errors, misclassification cost, and mean misclassification cost, to assess the performance of the proposed AdaBoostWithCost classifier. e empirical evaluation of this study demonstrates two significant aspects of benchmarking the performance of the AdaBoostWithCost algorithm against AdaBoost
Summary
Smartphones play a significant role in human life, and the number of mobile operators is rapidly increasing in every technologically advanced country. Computational Intelligence and Neuroscience algorithm developed for binary classification to improve accuracy It has become a somewhat feasible method for different kinds of boosting in machine learning paradigms. AdaBoost is inherently a cost-insensitive boosting algorithm; it has limited applications where costs need to be treated differently for different misclassification errors. E clairvoyant study empirically evaluates the AdaBoostWithCost cost-sensitive boosting method to predict customer churn rate with higher accuracy than the fundamental AdaBoost classifier. AdaBoost (adaptive boosting) is the first successful boosting algorithm developed for binary classification using this concept to achieve more accuracy It has become somewhat of a go-to method for different kinds of boosting in machine learning paradigms. AdaBoost fundamentally is not a cost-insensitive boosting algorithm; it has inherent limitations for applications where costs need to be treated differently for different misclassification errors. Researchers have put serious thought and significant attention to minimizing the misclassification cost instead of minimizing the errors. erefore, in recent years, cost-sensitive learning has been a common approach to solving this class imbalance problem
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