Detecting GSM churners by using Euclidean Indexing HDMR

Tunga, M. Alper; Karahoca, Adem

doi:10.1016/j.asoc.2014.11.001

Cited by 3 publications

(1 citation statement)

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“…Alper Tunga et al [10] introduced a polynomial based method Euclidean Indexing High Dimensional Model Representation (HDMR) for churn prediction. Initially, attribute filter 'Information gain' is applied to select necessary attributes.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Class Imbalance Problem in Customer Churn Prediction: A Novel Extension to Under-sampling

Salunkhe¹,

Mali²

2018

IJISA

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Abstract-Customer retention is becoming a key success factor for many business applications due to increasing market competition. Especially telecom companies are facing this challenge with a rapidly increasing number of service providers. Hence there is need to focus on customer churn prediction in order to detect the customers that are likely to churn i.e. switch from one service provider to another. Several data mining techniques are applied for classifying customers into the churn and non-churn category. But churn prediction applications comprise an imbalanced distribution of the dataset.One of the commonly used techniques to handle imbalanced data is re-sampling of data as it is independent of the classifier being used. In this paper, we develop a hybrid re-sampling approach named SOS-BUS by combining well known oversampling technique SMOTE with our novel under-sampling technique. Our methodology aims to focus on the necessary data of majority class and avoid their removal in order to overcome the limitation of random under-sampling. Experimental results show that the proposed approach outperforms the other reference techniques in terms of Area under ROC Curve (AUC).

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Section: Related Workmentioning

confidence: 99%