An empirical comparison of techniques for the class imbalance problem in churn prediction

Zhu, Bing; Baesens, Bart; Broucke, Seppe K. L. M. vanden

doi:10.1016/j.ins.2017.04.015

Cited by 130 publications

(62 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To sum up, for every fraud classifier, SMOTE-ENN outperforms the other four sampling techniques. This is not surprising that SMOTE-ENN is superior since it has already been shown in [28] that SMOTE-ENN performs outstandingly well on 11 real-world churn datasets.…”

Section: Classification With Balanced Sb Datasetsmentioning

confidence: 71%

Detection of Auction Fraud in Commercial Sites

Anowar

Sadaoui

2020

J. theor. appl. electron. commer. res.

View full text Add to dashboard Cite

Online auctions have become one of the most convenient ways to commit fraud due to a large amount of money being traded every day. Shill bidding is the predominant form of auction fraud, and it is also the most difficult to detect because it so closely resembles normal bidding behavior. Furthermore, shill bidding does not leave behind any apparent evidence, and it is relatively easy to use to cheat innocent buyers. Our goal is to develop a classification model that is capable of efficiently differentiating between legitimate bidders and shill bidders. For our study, we employ an actual training dataset, but the data are unlabeled. First, we properly label the shill bidding samples by combining a robust hierarchical clustering technique and a semi-automated labeling approach. Since shill bidding datasets are imbalanced, we assess advanced over-sampling, under-sampling and hybrid-sampling methods and compare their performances based on several classification algorithms. The optimal shill bidding classifier displays high detection and low misclassification rates of fraudulent activities.

show abstract

Section: Classification With Balanced Sb Datasetsmentioning

confidence: 71%

Detection of Auction Fraud in Commercial Sites

Anowar

Sadaoui

2020

J. theor. appl. electron. commer. res.

View full text Add to dashboard Cite

show abstract

“…There are many ways in which imbalanced information can be handled. According to [ 6], when conventional machine learning methods are applied to imbalanced information, the laws of induction defining the majority ideas are often stronger than those of the notion of minority. Class imbalance is studied on a large scale sparse data in a distributed environment, according to [ 9].…”

Section: A Backgroundmentioning

confidence: 99%

Synthetic Minority Oversampling and Smote Regularised Deep Autoencoders Neural Network Techniques for Fraud Prediction in Financial Payment Services

Smiles*¹,

Kumar²

2019

IJITEE

View full text Add to dashboard Cite

Abstract: Frauds in Financial Payment Services are the most prevalent form of cybercrime. The increased growth in e-commerce and mobile payments in recent years is behind the rising incidence of fraud in financial payment services. According to "McKinsey, fraud losses throughout the world could be close to $44 billion by 2025." Every year, fraudulent card transactions causes billions of US Dollar of loss. To reduce these losses, designing effective fraud detection algorithms is essential, which depend on sophisticated machine learning methods to help investigators in fraud. For banks and financial institutions, therefore, fraud detection systems have gained excellent significance. Though the fake transactions are very low when compared to genuine transaction, care must be taken to predict it so that the financial institutions can maintain the customer integrity. As fraud is unlikely to occur compared to normal operations, we have the class imbalance problem. We applied Synthetic Minority Oversampling TEchnique (SMOTE) and the Ensemble of sampling methods(Balanced Random Forest Classifier, Balanced Bagging Classifier, Easy Ensemble Classifier, RUS Boost) to Ensemble machine learning algorithms Performance assessment using sensitivity, specificity, precision, ROC area. The purpose of this article is to analyze different predictive models to see how precise they are to detect whether a transaction is a standard payment or a fraud. Instead of misclassifying a real transaction as fraud, this model seeks to improve detection of fraud. We noted that the technique of Ensemble learning using Maximum voting detects the fraud better than other classifiers. Decision Tree Classifier, Logistic Regression, Balanced Bagging classifier is combined and the proposed algorithm is OptimizedEnsembleFD Algorithm. The sample size is increased and deep learning is applied .It is found that the proposed system Smote Regularised Deep Autoencoders (SRD Autoencoders) neural network performs better with good recall and accuracy for this large dataset.

show abstract

“…1 is an illustration of the phenomenon of customer churn and its prediction approach. Most researchers have done a lot of works on churn since it emerged and a lot of techniques have been proposed as a result to tackle the problems of churn [7][8][9][10]. The most commonly used ones are SVM, decision tree, artificial neural networks (ANN), logistic regression, and so on.…”

Section: Related Workmentioning

confidence: 99%

Improving the Reliability of Churn Predictions in Telecommunication Sector by Considering Customer Region

Zhou¹,

Boateng²,

Amoh³

et al. 2019

IJITCS

View full text Add to dashboard Cite

Prediction of customer churn has been one of the most interesting and challenging tasks facing most telecommunication companies recently. For the past decade, researchers together with practitioners have been working and designing models that can correctly make more accurate customer churn predictions (CCP). However, most of these models have less accuracy than expected which is hugely affecting the intended purpose. Consequently, most of these CCP models add little or nothing to the revenue growth of telecommunication industries. This work aims at improving the reliability of CCP in the telecommunication sector. To achieve this objective, a new attribute to be factored in CCP, known as the regional churn rate (RCR), is presented. Basically, RCR gives an idea about the rate of churning in a particular locality or region. Thus, a prediction model with a more accurate CCP using a support vector machine (SVM) classifier is proposed. The performance of the proposed model is critically evaluated using five metrics i.e. misclassification error, precision, recall, specificity and f-measure. At the same time, the performance of the proposed classifier (CCP with RCR) is compared with another SVM classifier which doesn't consider RCR (CCP without RCR). Results show that the proposed model which considers the RCR of a customer's location gives the highest accuracies for four performance metrics i.e. precision, recall, misclassification error and f-measure. Therefore, the proposed SVM-based CCP model gives a more clear indication as to whether a customer is a potential churner or not.

show abstract

An empirical comparison of techniques for the class imbalance problem in churn prediction

Cited by 130 publications

References 58 publications

Detection of Auction Fraud in Commercial Sites

Detection of Auction Fraud in Commercial Sites

Synthetic Minority Oversampling and Smote Regularised Deep Autoencoders Neural Network Techniques for Fraud Prediction in Financial Payment Services

Improving the Reliability of Churn Predictions in Telecommunication Sector by Considering Customer Region

Contact Info

Product

Resources

About