Customer Churn Prediction in Telecom Sector: A Survey and way a head

© 2021 International Journal of Scientific & Technology Research. This work is licensed under a Creative Commons Attribution 4.0 International License.The telecommunication (telecom)industry is a highly technological domain has rapidly developed over the previous decades as a result of the commercial success in mobile communication and the internet. Due to the strong competition in the telecom industry market, companies use a business strategy to better understand their customers’ needs and measure their satisfaction. This helps telecom companies to improve their retention power and reduces the probability to churn. Knowing the reasons behind customer churn and the use of Machine Learning (ML) approaches for analyzing customers' information can be of great value for churn management. This paper aims to study the importance of Customer Churn Prediction (CCP) and recent research in the field of CCP. Challenges and open issues that need further research and development to CCP in the telecom sector are exploredPeer reviewe

Straggler handling approaches in mapreduce framework: a comparative study

The proliferation of information technology produces a huge amount of data called big data that cannot be processed by traditional database systems. These Various types of data come from different sources. However, stragglers are a major bottleneck in big data processing, and hence the early detection and accurate identification of stragglers can have important impacts on the performance of big data processing. This work aims to assess five stragglers identification methods: Hadoop native scheduler, LATE Scheduler, Mantri, MonTool, and Dolly. The performance of these techniques was evaluated based on three benchmarked methods: Sort, Grep and WordCount. The results show that the LATE Scheduler performs the best and it would be efficient to obtain better results for stragglers identification

An efficient churn prediction model using gradient boosting machine and metaheuristic optimization

© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, to view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Customer churn remains a critical challenge in telecommunications, necessitating effective churn prediction (CP) methodologies. This paper introduces the Enhanced Gradient Boosting Model (EGBM), which uses a Support Vector Machine with a Radial Basis Function kernel (SVMRBF) as a base learner and exponential loss function to enhance the learning process of the GBM. The novel base learner significantly improves the initial classification performance of the traditional GBM and achieves enhanced performance in CP-EGBM after multiple boosting stages by utilizing state-of-the-art decision tree learners. Further, a modified version of Particle Swarm Optimization (PSO) using the consumption operator of the Artificial Ecosystem Optimization (AEO) method to prevent premature convergence of the PSO in the local optima is developed to tune the hyper-parameters of the CP-EGBM effectively. Seven open-source CP datasets are used to evaluate the performance of the developed CP-EGBM model using several quantitative evaluation metrics. The results showed that the CP-EGBM is significantly better than GBM and SVM models. Results are statistically validated using the Friedman ranking test. The proposed CP-EGBM is also compared with recently reported models in the literature. Comparative analysis with state-of-the-art models showcases CP-EGBM's promising improvements, making it a robust and effective solution for churn prediction in the telecommunications industry.Peer reviewe

Anovel HEOMGA Approach for Class Imbalance Problem in the Application of Customer Churn Prediction

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s42979-021-00850-yMaking class balance is essential when learning from highly skewed datasets; otherwise, a learner may classify all instances to a negative class, resulting in a high false-negative rate. As a result, a precise balancing strategy is required. Many researchers have investigated class imbalance using Machine Learning (ML) methods due to their powerful generalization performance and interpreting capabilities, comparing with random sampling techniques, to handle the problem of class imbalance in the preprocessing phase to facilitate learning process and improve performance results of learners. In this research, an effective method called HEOMGA is presented by combining Heterogeneous Euclidean-Overlap Metric (HEOM) and Genetic Algorithm (GA) for oversampling minority class. The HEOM is employed to define a fitness function for the GA. To assess the performance of the proposed HEOMGA method, three benchmark datasets from UCI repository in the domain of customer churn prediction are examined using three different ML learners and evaluated with three performance metrics. The experiment results show the effectiveness of the proposed method compared to some popular oversample methods, such as SMOTE, ADASYN, G SMOTE, and Gaussian oversampling methods. The HEOMGA method significantly outperformed the other oversampling methods in terms of recall, G mean, and AUC when the Wilcoxon signed-rank test is used.Peer reviewe