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Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees
Research article (2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), 2020) · cited 20× · AI/ML
Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees
Summary
Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees is a scholarly article[1].
Key Facts
Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees. Retrieved May 24, 2026, from https://4ort.xyz/entity/handling-class-imbalance-in-customer-churn-prediction-in-telecom-sector-using-sampling-techniques-bagging-and-boosting-t
MLA“Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/handling-class-imbalance-in-customer-churn-prediction-in-telecom-sector-using-sampling-techniques-bagging-and-boosting-t.
BibTeX@misc{4ortxyz_handling-class-imbalance-in-customer-churn-prediction-in-telecom-sector-using-sampling-techniques-bagging-and-boosting-t_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees}}, year = {2026}, url = {https://4ort.xyz/entity/handling-class-imbalance-in-customer-churn-prediction-in-telecom-sector-using-sampling-techniques-bagging-and-boosting-t}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees — https://4ort.xyz/entity/handling-class-imbalance-in-customer-churn-prediction-in-telecom-sector-using-sampling-techniques-bagging-and-boosting-t (retrieved 2026-05-24)
