Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The Pareto/NBD method has been actively used as a probability model for deriving the CLV until recently.
“The Pareto/NBD method has been actively used as a probability model for deriving the CLV until recently [19] [147] [148].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [147]
Cited by
Sorted by citations
A Survey on Churn Analysis in Various Business Domains
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3042657 · Cited by 108 · see citing works
Customer Churn Prediction in Superannuation: A Sequential Pattern Mining Approach
Culbert, B., Fu, B., Brownlow, J., Chu, C., Meng, Q., & Xu, G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-92013-9_10 · Cited by 2 · see citing works
- Authors
- Idris, A., Rizwan, M., & Khan, A.
- Affiliations
- Pakistan Institute of Engineering and Applied Sciences (PK)
- Corresponding author
- Khan, Asifullah
- Venue
- Computers & Electrical Engineering
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 38
- Issue
- 6
- Pages
- 1808-1819
- ISSN
- 0045-7906 (ISSN-L)
- OpenAlex
- W2065601393
- Cited by
- 139
- References
- 0
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 10 · 2023: 12 · 2022: 11 · 2021: 11 · 2020: 14 · 2019: 8 · 2018: 13 · 2017: 17 · 2016: 5 · 2015: 7 · 2014: 4 · 2013: 5
- FWCI
- 14.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Electrical Engineering (publishedVersion)
- Funders
- Higher Education Commission, Pakistan
- Higher Education Commision, Pakistan
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Keywords
- customer churn prediction, telecommunications, class imbalance, undersampling, particle swarm optimization, feature selection, minimum redundancy maximum relevance, principal component analysis, random forest, k-nearest neighbors, customer retention
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
