Enhanced Prediction Model for Customer Churn in Telecommunication Using EMOTE

Babu, S.; Ananthanarayanan, N. R.

doi:10.1007/978-981-10-5520-1_43

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…Theorem 1. Let f k,u be the under-sampling k-NN classifier defined as in (8), where the acceptance probability is chosen as in (6). Assume that P satisfies Assumptions 1 and P X is the uniform distribution on [0, 1] d .…”

Section: Results On Convergence Rates For the Under-sampling K-nn Cla...mentioning

confidence: 99%

“…Proposition 4. Let f k,u be the under-sampling k-NN classifier defined by (8). Assume that P X is the uniform distribution on [0, 1] d and Assumption 1 is satisfied.…”

Section: Bounding the Approximation Error Termmentioning

confidence: 99%

“…Imbalanced classification has been encountered in multiple areas such as telecommunication managements [8,4], bioinformatics [16,69], fraud detection [50,72], and medical diagnosis [93], and has been considered one of the top ten problems in data mining research [87,65]. In fact, the ratio of the size of the majority class to the minority class can be as huge as 10 6 [84].…”

Section: Introductionmentioning

confidence: 99%

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Under-bagging Nearest Neighbors for Imbalanced Classification

Hang¹,

Cai²,

Yang³

et al. 2021

Preprint

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In this paper, we propose an ensemble learning algorithm called under-bagging k-nearest neighbors (under-bagging k-NN ) for imbalanced classification problems. On the theoretical side, by developing a new learning theory analysis, we show that with properly chosen parameters, i.e., the number of nearest neighbors k, the expected sub-sample size s, and the bagging rounds B, optimal convergence rates for under-bagging k-NN can be achieved under mild assumptions w.r.t. the arithmetic mean (AM) of recalls. Moreover, we show that with a relatively small B, the expected sub-sample size s can be much smaller than the number of training data n at each bagging round, and the number of nearest neighbors k can be reduced simultaneously, especially when the data are highly imbalanced, which leads to substantially lower time complexity and roughly the same space complexity. On the practical side, we conduct numerical experiments to verify the theoretical results on the benefits of the under-bagging technique by the promising AM performance and efficiency of our proposed algorithm.

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Section: Results On Convergence Rates For the Under-sampling K-nn Cla...mentioning

confidence: 99%

“…Proposition 4. Let f k,u be the under-sampling k-NN classifier defined by (8). Assume that P X is the uniform distribution on [0, 1] d and Assumption 1 is satisfied.…”

Section: Bounding the Approximation Error Termmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Under-bagging Nearest Neighbors for Imbalanced Classification

Hang¹,

Cai²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…They reported the bagged stacked performs well in both datasets with 98.4% and 97.2% of accuracies. In 2018, [52] Ammar et al created ensemble stacking with bench mark algorithms and integrated cost-effective mechanism. They applied on UCI churn dataset.…”

Section: A Traditional Single Classifier Methodsmentioning

confidence: 99%

“…They introduced this classifier for profitability and interpretability in churn prediction model. They used 9 different dataset and reported ProfTree algorithm yields good EMPC value when compared to other tree-based classifiers in the year 2018.In 2018, S. Babu et al [52] proposed algorithms for class imbalance issue by enhanced SMOTE and DT. They achieved higher accuracy on UCI churn dataset using those algorithms.…”

Section: Systematic Analysis Procedures For Electing Articlesmentioning

confidence: 99%

Exploring Hybrid and Ensemble Models for Customer Churn Prediction in Telecom Sector

Pamina¹,

Rajkumar²,

Kiruthika³

et al. 2019

IJRTE

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Most prominent challenges in all business is to retain and satisfy their valuable customers for sustain successfully in the market. Numerous Machine learning approaches are emerging to develop various customer retention models to solve this issue in many applications. This swing is more realized in telecom industry due its enormous significance. This article presents an elaborated survey on machine learning based churn prediction in telecom sector from the year 2000 to 2018. We also extracted the problems and challenges in Telecom Churn Prediction and reported suggestion and solutions. We believe this article helps the researches or data analysts in the telecom field to select optimal and appropriate methods and for designing improved novel model for churn prediction in future

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