Abstract
Customer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining the customers. CCP in the telecommunication sector has become an essential need owing to a rise in the number of the telecommunication service providers. Recently, machine learning (ML) and deep learning (DL) models have begun to develop effective CCP model. This paper presents a new improved synthetic minority over-sampling technique (SMOTE) with optimal weighted extreme machine learning (OWELM) called the ISMOTE-OWELM model for CCP. The presented model comprises preprocessing, balancing the unbalanced dataset, and classification. The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM. Initially, the customer data involve data normalization and class labeling. Then, the ISMOTE is employed to handle the imbalanced dataset where the rain optimization algorithm (ROA) is applied to determine the optimal sampling rate. At last, the WELM model is applied to determine the class labels of the applied data. Extensive experimentation is carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset. The simulation outcome portrayed that the ISMOTE-OWELM model is superior to other models with the accuracy of 0.94, 0.92, 0.909 on the applied dataset I, II, and III, respectively.
Highlights
Customer retention plays an important role in churn prediction, and telecommunication organization is directly associated with financial companies
A series of simulations are carried out to ensure the ISMOTE-OWELM model against the customer churn prediction (CCP) Telecommunication dataset
The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM
Summary
Customer retention plays an important role in churn prediction, and telecommunication organization is directly associated with financial companies. Telecommunication firms have applied an extensive range of tricks like developing a collection of services, providing various ideas to satisfy the user demands, and offering discounts to retain user trustworthiness and inspire novel clients, wherein the profit can be enhanced gradually. These tactics result in enhanced competition among several firms, and the prediction of user behavior is a complex operation. It prefers to offer a modified service to maintain the users preventing customer churn and retain the increased competition. A series of simulations are carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset
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