Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems [version 2; peer review: 1 approved, 2 approved with reservations]
Background: Customer churn prediction (CCP) refers to detecting which customers are likely to cancel the services provided by a service provider, for example, internet services. The class imbalance problem (CIP) in machine learning occurs when there is a huge difference in the samples of the positiv...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
F1000 Research Ltd
2022-06-01
|
| Series: | F1000Research |
| Subjects: | |
| Online Access: | https://f1000research.com/articles/10-988/v2 |
| _version_ | 1817980796469772288 |
|---|---|
| author | Maw Maw Su-Cheng Haw Chin-Kuan Ho |
| author_facet | Maw Maw Su-Cheng Haw Chin-Kuan Ho |
| author_sort | Maw Maw |
| collection | DOAJ |
| description | Background: Customer churn prediction (CCP) refers to detecting which customers are likely to cancel the services provided by a service provider, for example, internet services. The class imbalance problem (CIP) in machine learning occurs when there is a huge difference in the samples of the positive class compared to the negative class. It is one of the major obstacles in CCP as it deteriorates performance in the classification process. Utilizing data sampling techniques (DSTs) helps to resolve the CIP to some extent. Methods: In this paper, we review the effect of using DSTs on algorithmic fairness, i.e., to investigate whether the results pose any discrimination between male and female groups and compare the results before and after using DSTs. Three real-world datasets with unequal balancing rates were prepared and four ubiquitous DSTs were applied to them. Six popular classification techniques were utilized in the classification process. Both classifier’s performance and algorithmic fairness are evaluated with notable metrics. Results: The results indicated that the Random Forest classifier outperforms other classifiers in all three datasets and, that using SMOTE and ADASYN techniques causes more discrimination in the female group. The rate of unintentional discrimination seems to be higher in the original data of extremely unbalanced datasets under the following classifiers: Logistics Regression, LightGBM, and XGBoost. Conclusions: Algorithmic fairness has become a broadly studied area in recent years, yet there is very little systematic study on the effect of using DSTs on algorithmic fairness. This study presents important findings to further the use of algorithmic fairness in CCP research. |
| first_indexed | 2024-04-13T22:57:48Z |
| format | Article |
| id | doaj.art-949971e5dc10471a88e59c36c794e40b |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-04-13T22:57:48Z |
| publishDate | 2022-06-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-949971e5dc10471a88e59c36c794e40b2022-12-22T02:25:57ZengF1000 Research LtdF1000Research2046-14022022-06-0110134711Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems [version 2; peer review: 1 approved, 2 approved with reservations]Maw Maw0Su-Cheng Haw1https://orcid.org/0000-0002-7190-0837Chin-Kuan Ho2Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, MalaysiaBackground: Customer churn prediction (CCP) refers to detecting which customers are likely to cancel the services provided by a service provider, for example, internet services. The class imbalance problem (CIP) in machine learning occurs when there is a huge difference in the samples of the positive class compared to the negative class. It is one of the major obstacles in CCP as it deteriorates performance in the classification process. Utilizing data sampling techniques (DSTs) helps to resolve the CIP to some extent. Methods: In this paper, we review the effect of using DSTs on algorithmic fairness, i.e., to investigate whether the results pose any discrimination between male and female groups and compare the results before and after using DSTs. Three real-world datasets with unequal balancing rates were prepared and four ubiquitous DSTs were applied to them. Six popular classification techniques were utilized in the classification process. Both classifier’s performance and algorithmic fairness are evaluated with notable metrics. Results: The results indicated that the Random Forest classifier outperforms other classifiers in all three datasets and, that using SMOTE and ADASYN techniques causes more discrimination in the female group. The rate of unintentional discrimination seems to be higher in the original data of extremely unbalanced datasets under the following classifiers: Logistics Regression, LightGBM, and XGBoost. Conclusions: Algorithmic fairness has become a broadly studied area in recent years, yet there is very little systematic study on the effect of using DSTs on algorithmic fairness. This study presents important findings to further the use of algorithmic fairness in CCP research.https://f1000research.com/articles/10-988/v2Customer churn prediction Data sampling techniques Algorithmic fairness Class imbalance problem eng |
| spellingShingle | Maw Maw Su-Cheng Haw Chin-Kuan Ho Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems [version 2; peer review: 1 approved, 2 approved with reservations] F1000Research Customer churn prediction Data sampling techniques Algorithmic fairness Class imbalance problem eng |
| title | Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_full | Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_fullStr | Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_full_unstemmed | Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_short | Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_sort | utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems version 2 peer review 1 approved 2 approved with reservations |
| topic | Customer churn prediction Data sampling techniques Algorithmic fairness Class imbalance problem eng |
| url | https://f1000research.com/articles/10-988/v2 |
| work_keys_str_mv | AT mawmaw utilizingdatasamplingtechniquesonalgorithmicfairnessforcustomerchurnpredictionwithdataimbalanceproblemsversion2peerreview1approved2approvedwithreservations AT suchenghaw utilizingdatasamplingtechniquesonalgorithmicfairnessforcustomerchurnpredictionwithdataimbalanceproblemsversion2peerreview1approved2approvedwithreservations AT chinkuanho utilizingdatasamplingtechniquesonalgorithmicfairnessforcustomerchurnpredictionwithdataimbalanceproblemsversion2peerreview1approved2approvedwithreservations |