کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
454074 | 695093 | 2012 | 12 صفحه PDF | دانلود رایگان |
The telecommunication industry faces fierce competition to retain customers, and therefore requires an efficient churn prediction model to monitor the customer’s churn. Enormous size, high dimensionality and imbalanced nature of telecommunication datasets are main hurdles in attaining the desired performance for churn prediction. In this study, we investigate the significance of a Particle Swarm Optimization (PSO) based undersampling method to handle the imbalance data distribution in collaboration with different feature reduction techniques such as Principle Component Analysis (PCA), Fisher’s ratio, F-score and Minimum Redundancy and Maximum Relevance (mRMR). Whereas Random Forest (RF) and K Nearest Neighbour (KNN) classifiers are employed to evaluate the performance on optimally sampled and reduced features dataset. Prediction performance is evaluated using sensitivity, specificity and Area under the curve (AUC) based measures. Finally, it is observed through simulations that our proposed approach based on PSO, mRMR, and RF termed as Chr-PmRF, performs quite well for predicting churners and therefore can be beneficial for highly competitive telecommunication industry.
Figure optionsDownload as PowerPoint slideHighlights
► Telecom industry faces fierce completion to retain customers.
► Enormous size, imbalanced dataset and high dimensionality make churn prediction in telecom a challenging problem.
► Our proposed approach named Chr-PmRF, employs PSO based balancing, mRMR feature reduction and Random Forest as a classifier.
► Chr-PmRF efficiently predicts churners and might be beneficial for highly competitive telecommunication industry.
Journal: Computers & Electrical Engineering - Volume 38, Issue 6, November 2012, Pages 1808–1819