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

Al-Shourbaji, Ibrahim; Helian, Na; Sun, Yi; Alhameed, Mohammed

doi:10.1007/s42979-021-00850-y

Cited by 8 publications

(6 citation statements)

References 40 publications

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“…The characteristics of these Datasets (DSs) are presented in Table 1 . The HEOMGA 33 is used for data balancing and ACO-RSA 34 is employed for FS on all the datasets. Possible bias in selecting the training and testing datasets is avoided using the tenfold cross-validation (CV) technique is employed.…”

Section: Resultsmentioning

confidence: 99%

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

AlShourbaji,

Helian,

Sun

et al. 2023

Sci Rep

Self Cite

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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.

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Section: Resultsmentioning

confidence: 99%

“…A dataset balance can be checked by comparing the number of examples for each class label . For balancing the dataset, the minority class examples are oversampled to match the number of examples using the Heterogeneous Euclidean-Overlap Metric Genetic Algorithm (HEOMGA) approach 33 .…”

Section: Proposed Cp-egbmmentioning

confidence: 99%

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

AlShourbaji,

Helian,

Sun

et al. 2023

Sci Rep

Self Cite

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“…Class imbalance is a situation where the number of observations belonging to one class is significantly lower than those belonging to the other class. As stated by the study in [24], random sampling methods for class imbalance are not useful in improving the performance of predicting results. erefore, a controlled-ratio undersampling strategy is employed in this study to favor both minority and majority classes [24].…”

Section: Introductionmentioning

confidence: 99%

“…As stated by the study in [24], random sampling methods for class imbalance are not useful in improving the performance of predicting results. erefore, a controlled-ratio undersampling strategy is employed in this study to favor both minority and majority classes [24]. To evaluate the general performance of the predicted models, six evaluation metrics of Accuracy, Recall, Precision, AUC, F1-score and Mean Absolute Error (MAE) were used [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study

Edwine

Wang

Song

et al. 2022

Mathematical Problems in Engineering

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Churn rate describes the rate at which customers abandon a product or service. Identifying churn-risk customers is essential for telecom sectors to retain old customers and maintain a higher competitive advantage. The purpose of this paper is to explore an effective method for detecting the risk of customer churn in telecom sectors through comparing the advanced machine learning methods and their optimization algorithms. Based on two different telecom datasets, Mutual Information classifier was firstly utilized to select the most critical features relevant to customer churn. Next, the controlled-ratio undersampling strategy was employed to balance both minority and majority classes. Key hyperparameter optimization algorithms of Grid Search, Random Search, and Genetic Algorithms were then combined to fit the three promising machine learning models-Random Forest, Support Vector Machines, and K-nearest neighbors into the customer churn prediction problem. Six evaluation metrics-Accuracy, Recall, Precision, AUC, F1-score and Mean Absolute Error, were last used to evaluate the performance of the proposed models. The experimental results have revealed that the RF algorithm optimized by Grid Search based on a low-ratio undersampling strategy (RF-GS-LR) outperformed other models in extracting hidden information and understanding future churning behaviors of customers on both datasets, with the maximum accuracy of 99% and 95% on the applied dataset 1-2 respectively.

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“…By formulating marketing plans based on these data, enterprise can avoid the loss of old customers and increase more economic benefits [3]. Some scholars have pointed out that enterprises will accumulate massive amounts of data in the daily operation process, from which valuable information can be mined and customers who are about to be lost can be accurately predicted [4]. For e-commerce companies, when they find customers who are about to be lost, taking corresponding retention measures can effectively reduce the loss rate of corporate customers and make customers active on the company's e-commerce platform [5].…”

Section: Introductionmentioning

confidence: 99%

Research on Improved Xgboost Algorithm for Big Data Analysis of e-Commerce Customer Churn

Li¹

2022

IJACSA

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With the increasing cost of acquiring new users for e-commerce enterprises, it has become an important task for e-commerce enterprises to actively carry out customer churn management. Therefore, based on the distributed gradient enhancement library algorithm (XGBoost), this research proposes a big data analysis study on e-commerce customer churn. First, it conducts an evaluation analysis on e-commerce customer segmentation and combines the random forest algorithm (RF) to build an RF XGBoost prediction model based on customer churn. Finally, it verifies the performance of the prediction model. The results show that the area under receiver operating characteristic curve (AUC) value, prediction accuracy, recall rate, and F1 value of the RF-XGBoost model are significantly better than those of the RF, XGBoost, and ID3 decision trees to build an e-commerce customer churn prediction model; The average output error of RF-XGBoost model is 0.42, and the average output error is relatively good, indicating that the model proposed in this study has a smaller error and higher accuracy. It can make a general assessment of the customer churn of e-commerce enterprises, and then provide data support for the customer maintenance work of e-commerce enterprises. It is helpful to analyze the relevant factors affecting customer churn, to Equationte targeted customer service programs, thus improving the economic benefits of e-commerce enterprises.

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

Cited by 8 publications

References 40 publications

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

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

Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study

Research on Improved Xgboost Algorithm for Big Data Analysis of e-Commerce Customer Churn

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