Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks
The customer churn is one of the key challenges for enterprises, and market saturation and increased competition to maintain business position has caused companies to make all attempts to identify customers who are likely to leave and end their relationship with a company in a particular period to b...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/9390768 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832565340676030464 |
|---|---|
| author | Maryam Rahmaty Amir Daneshvar Fariba Salahi Maryam Ebrahimi Adel Pourghader Chobar |
| author_facet | Maryam Rahmaty Amir Daneshvar Fariba Salahi Maryam Ebrahimi Adel Pourghader Chobar |
| author_sort | Maryam Rahmaty |
| collection | DOAJ |
| description | The customer churn is one of the key challenges for enterprises, and market saturation and increased competition to maintain business position has caused companies to make all attempts to identify customers who are likely to leave and end their relationship with a company in a particular period to become the customer of another company. In recent years, many methods have been developed including data mining for predicting the customer churn and manners that customers are likely to behave in the future and therefore, taking action early to prevent their leaving. This study proposes a hybrid system based on fuzzy entropy criterion selection algorithm with similar classifiers, grey wolf optimization algorithm, and artificial neural network to predict the customer churn of those companies that suffer losses from losing customers over time. The research results are evaluated by other methods in the criteria of accuracy, recall, precision, and F_measure, and it is declared that the proposed method is superior over other methods. |
| format | Article |
| id | doaj-art-ee07a0896a4e412fbdd5dccd71db1852 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-ee07a0896a4e412fbdd5dccd71db18522025-02-03T01:08:45ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/9390768Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural NetworksMaryam Rahmaty0Amir Daneshvar1Fariba Salahi2Maryam Ebrahimi3Adel Pourghader Chobar4Department of ManagementDepartment of Information Technology ManagementDepartment of Industrial ManagementDepartment of Information Technology ManagementDepartment of Industrial EngineeringThe customer churn is one of the key challenges for enterprises, and market saturation and increased competition to maintain business position has caused companies to make all attempts to identify customers who are likely to leave and end their relationship with a company in a particular period to become the customer of another company. In recent years, many methods have been developed including data mining for predicting the customer churn and manners that customers are likely to behave in the future and therefore, taking action early to prevent their leaving. This study proposes a hybrid system based on fuzzy entropy criterion selection algorithm with similar classifiers, grey wolf optimization algorithm, and artificial neural network to predict the customer churn of those companies that suffer losses from losing customers over time. The research results are evaluated by other methods in the criteria of accuracy, recall, precision, and F_measure, and it is declared that the proposed method is superior over other methods.http://dx.doi.org/10.1155/2022/9390768 |
| spellingShingle | Maryam Rahmaty Amir Daneshvar Fariba Salahi Maryam Ebrahimi Adel Pourghader Chobar Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks Discrete Dynamics in Nature and Society |
| title | Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks |
| title_full | Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks |
| title_fullStr | Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks |
| title_full_unstemmed | Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks |
| title_short | Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks |
| title_sort | customer churn modeling via the grey wolf optimizer and ensemble neural networks |
| url | http://dx.doi.org/10.1155/2022/9390768 |
| work_keys_str_mv | AT maryamrahmaty customerchurnmodelingviathegreywolfoptimizerandensembleneuralnetworks AT amirdaneshvar customerchurnmodelingviathegreywolfoptimizerandensembleneuralnetworks AT faribasalahi customerchurnmodelingviathegreywolfoptimizerandensembleneuralnetworks AT maryamebrahimi customerchurnmodelingviathegreywolfoptimizerandensembleneuralnetworks AT adelpourghaderchobar customerchurnmodelingviathegreywolfoptimizerandensembleneuralnetworks |