Customer churn prediction model in cloud environment using DFE-WUNB: ANN deep feature extraction with Weight Updated Tuned Naïve Bayes classification with Block-Jacobi SVD dimensionality reduction

Arockia Panimalar, S.; Krishnakumar, A.

doi:10.1016/j.engappai.2023.107015

Engineering Applications of Artificial Intelligence

2023

DOI: 10.1016/j.engappai.2023.107015

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Customer churn prediction model in cloud environment using DFE-WUNB: ANN deep feature extraction with Weight Updated Tuned Naïve Bayes classification with Block-Jacobi SVD dimensionality reduction

S. Arockia Panimalar,

A. Krishnakumar

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Cited by 10 publications

References 28 publications

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A novel ZIF-8 mediated nanocomposite colorimetric sensor array for rapid identification of matcha grades, validated by density functional theory

Wang,

Shoaib,

Wang

et al. 2025

Journal of Food Composition and Analysis

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A novel ZIF-8 mediated nanocomposite colorimetric sensor array for rapid identification of matcha grades, validated by density functional theory

Wang,

Shoaib,

Wang

et al. 2025

Journal of Food Composition and Analysis

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Customer Personality Analysis for Churn Prediction Using Hybrid Ensemble Models and Class Balancing Techniques

Ahmad,

Awan,

Nobanee

et al. 2024

IEEE Access

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Today's businesses rely heavily on focused marketing to improve their chances of growing and keeping their consumer base. Internet behemoths like Google and Facebook have expanded their business models around targeted advertisements that support business growth. Customer personality identification helps for churn prediction for companies. This problem arises in several companies where customer leaves companies for many reasons. This gap leads to conduct study for customer personality analysis. The collected dataset was highly imbalanced in nature. Two class balancing approaches CTGAN (Conditional tabular Generative adversarial networks) and SMOTE (Synthetic minority oversampling technique) has been utilized to equalize the both classes. There are three ensemble approaches such as bagging, boosting and stacking have been utilized for modeling purpose bagging approach uses Random Forest (RF) boosting utilizes XGBoost (XGB), Light Gradient Boosting Machine (LGBM) and ADA Boost (ADA B). The proposed Hybrid Model HSLR comprises of RF, XGB, ADA Boost, LGBM approaches as base classifiers and LR as a Meta classifier. Three testing independent set, k-fold with 5 and 10 folds have been utilized. To evaluate the performance of classifiers evaluation metrics such as Accuracy score, Precision, Recall, F1 score, MCC and ROC score have been utilized. The SMOTE generated data has shown results as compare with CTGAN generated data. The SMOTE approach has shown the highest results of 94.06, 94.23, 94.28, 94.05, 88.13 and 0.984 as accuracy score, Precision, recall, F1, MCC and Roc score respectively.

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ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry

Saha,

Haque

et al. 2024

IEEE Access

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In the Telecommunication Industry (TCI) customer churn is a significant issue because the revenue of the service provider is highly dependent on the retention of existing customers. In this competitive market, it is essential for the service providers to figure out the concerns of their existing customers regarding their services as the cancellation of the services by the customers and switching to new service providers will not bring any good to the service provider. In the context of TCI, numerous research have been made to predict customer churn though, after the performance evaluation of these studies, it shows that there is enough room for progress. Therefore, in this study, we proposed a novel customer churn prediction architecture namely ChurnNet to predict customer churn in TCI. In our proposed ChurnNet, the 1D convolution layer is integrated with residual block, squeeze and excitation block, and spatial attention module to improve the performance. Residual block aids in solving the vanishing gradient problem. Squeeze and excitation block and spatial attention module enable the ChurnNet to understand the interdependency between and within the channels respectively. To evaluate the performance, the experiment is performed on 3 publicly available datasets. As the datasets have significant class imbalance issues, three data balancing techniques such as SMOTE, SMOTEEN, and SMOTETomek are performed. Along with 10-fold crossvalidation and after going through the rigorous experiment it was found that ChurnNet performed better than the state-of-the-art and obtained 95.59%, 96.94%, and 97.52% accuracy on 3 benchmark datasets respectively.INDEX TERMS Squeeze and excitation, spatial attention, residual block, churn prediction, ChurnNet.

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Customer churn prediction model in cloud environment using DFE-WUNB: ANN deep feature extraction with Weight Updated Tuned Naïve Bayes classification with Block-Jacobi SVD dimensionality reduction

Cited by 10 publications

References 28 publications

A novel ZIF-8 mediated nanocomposite colorimetric sensor array for rapid identification of matcha grades, validated by density functional theory

A novel ZIF-8 mediated nanocomposite colorimetric sensor array for rapid identification of matcha grades, validated by density functional theory

Customer Personality Analysis for Churn Prediction Using Hybrid Ensemble Models and Class Balancing Techniques

ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry

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