Customer churn prediction for telecommunication: Employing various various features selection techniques and tree based ensemble classifiers

Adnan Idris · Asifullah Khan

35 citations0/0 files storedInternational Multi-Topic Conference

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Customer churn and segmentation Artificial Intelligence in Healthcare Data Mining and Machine Learning Applications

Abstract

Ensemble classifiers have received increasing attention for attaining the higher classification performance in recent times. In this paper, we present comparative performances of various tree based ensemble classifiers in collaboration with maximum relevancy and minimum redundancy (mRMR), Fisher's ratio and F-score based features selection schemes for a challenging problem of churn prediction in telecommunication. The large sized telecommunication dataset has been the main hurdle in achieving the desired classification performance in the contemporary proposed churn prediction models. Though, tree based ensemble classifiers are considered suitable for larger datasets, but we have found rotation forest and rotboost as effective techniques compared to random forest, which employ boosting through features selection and increased diversity by incorporating linear feature extraction method such as Principal Component Analysis. In addition to the features selection performed by used ensembles, we have also incorporated mRMR, Fisher's ratio and F-score techniques for features selection. mRMR returns a coherent and well discriminants feature set, compared to Fisher's ratio and F-score, which significantly reduces the computations and helps classifier in attaining improved performance. The performance evaluation is conducted using area under curve, sensitivity and specificity where Rotboost, an ensemble of rotation forest and Adaboost in collaboration with mRMR has shown competitive results for churn prediction in telecommunication as compared to other ensemble methods.

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References

2003The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration 2008Customer churn prediction using improved balanced random forests 2006Bagging and Boosting Classification Trees to Predict Churn 2004Mining the customer credit using classification and regression tree and multivariate adaptive regression splines 2000Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry 2011An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction 2004Toward a successful CRM: variable selection, sampling, and ensemble 2009Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn

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2016Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods 2017Churn Prediction in Telecommunication Industry using Decision Tree 2021Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry 2022A New Churn Prediction Model Based on Deep Insight Features Transformation for Convolution Neural Network Architecture and Stacknet

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