A Comparison of Two Oversampling Techniques (SMOTE vs MTDF) for Handling Class Imbalance Problem: A Case Study of Customer Churn Prediction

Amin, Adnan; Rahim, Faisal; Ali, Imtiaz; Khan, Changez; Anwar, Sajid

doi:10.1007/978-3-319-16486-1_22

Cited by 16 publications

(7 citation statements)

References 21 publications

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“…"Class Imbalance is a typical theme on employee churn prediction [7], [8]...)," which is a similar point to the one in this paper. Standardized arrangements include subsampling systems to smoothen the ambiguities between the classes.…”

Section: A Class Imbalance Correctionsupporting

confidence: 73%

Foreseeing Employee Attritions using Diverse Data Mining Strategies

Vasa¹,

Masrani²

2019

IJRTE

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“Employee turnover is a noteworthy matter in knowledge-based companies.” On the off chance that employee leaves, they carry with them tacit information, often a source of competitive benefit to the other firms. Keeping in mind the end goal, to stay in the market and retain its employees, an organization requires minimizing employee attrition. This article discusses the employee churn/attrition forecast model using various methods of Machine Learning. Model yields are then scrutinized to outline and experiment the best practices on employee withholding at different stages of the employee’s association with an organization. This work has the potential for outlining better employee retention designs and enhancing employee contentment. This paper incorporates and condenses the capacity to gain from information and give information-driven experiences, choice, and forecasts and thinks about significant machine learning systems that have been utilized to create predictive churn models.

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Section: A Class Imbalance Correctionsupporting

confidence: 73%

Foreseeing Employee Attritions using Diverse Data Mining Strategies

Vasa¹,

Masrani²

2019

IJRTE

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“…Class Imbalance is a common theme on customer churn prediction ( [27], [28]...), which is a topic similar to the one developed in this paper. Standard solutions involve subsampling techniques to smooth the disparities between the observed classes.…”

Section: Class Imbalance Correctionmentioning

confidence: 98%

Employee turnover prediction and retention policies design: a case study

Ribes¹,

Touahri²,

Perthame³

2017

Preprint

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This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design & test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper.

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“…In [11], other model has been suggested that considers the imbalance behavior to work with churn detection. A sample technique that works effectively across the churn data proposed in [12]. In Game theory based CCP models are established.…”

Section: Misclassified Reduced Instance and Stochastic Gradient Descent With Logistic Regression Model For Customer Churn Predictionmentioning

confidence: 99%

Misclassified Reduced Instance and Stochastic Gradient Descent with Logistic Regression Model for Customer Churn Prediction

Amali*,

kumar,

Mohan

2020

IJITEE

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Customer Churn Prediction (CCP) is a difficult problem found to be helpful to make decisions due to the rapid growth in the number of telecom providers. At present, deep learning models are familiar because of the significant improvement in different areas. In this paper, a deep learning based CCP is introduced by the use of Stochastic Gradient Boosting (SGD) with Logistic regression (LR) classifier model. By the integration of SGD and LR, effective classification can be accomplished. To further improve the classifier efficiency, misclassified instances are removed from the dataset. Then, the processed data is again provided as input to the classification model. The presented SGD-LR model is validated on a benchmark dataset and the results are examiner with respect to different measures. The experimental outcome pointed out the projected model is superior to available CCP models on the identical dataset.

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A Comparison of Two Oversampling Techniques (SMOTE vs MTDF) for Handling Class Imbalance Problem: A Case Study of Customer Churn Prediction

Cited by 16 publications

References 21 publications

Foreseeing Employee Attritions using Diverse Data Mining Strategies

Foreseeing Employee Attritions using Diverse Data Mining Strategies

Employee turnover prediction and retention policies design: a case study

Misclassified Reduced Instance and Stochastic Gradient Descent with Logistic Regression Model for Customer Churn Prediction

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