A Three-Way Decision-Making Approach for Customer Churn Prediction Using Game-Theoretic Rough Sets
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-1045-5_1
Reference11 articles.
1. Mishra, Abinash and U. Srinivasulu Reddy. 2017. A novel approach for churn prediction using deep learning. In 2017 IEEE international conference on computational intelligence and computing research (ICCIC), 1–4. Piscataway, NJ: IEEE.
2. Ning, Lu, Hua Lin, Lu Jie, and Guangquan Zhang. 2012. A customer churn prediction model in telecom industry using boosting. IEEE Transactions on Industrial Informatics 10 (2): 1659–1665.
3. Bi, Wenjie, Meili Cai, Mengqi Liu, and Guo Li. 2016. A big data clustering algorithm for mitigating the risk of customer churn. IEEE Transactions on Industrial Informatics 12 (3): 1270–1281.
4. Amin, Adnan, Saeed Shehzad, Changez Khan, Imtiaz Ali, and Sajid Anwar. 2015. Churn prediction in telecommunication industry using rough set approach. In New trends in computational collective intelligence, 83–95. Berlin: Springer.
5. Abbas, Syed Manzar, Khubaib Amjad Alam, and Kwang-Man Ko. 2020. A three-way classification with game-theoretic n-soft sets for handling missing ratings in context-aware recommender systems. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–8. Glasgow: IEEE.
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