Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Class imbalances versus small disjuncts
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Evaluation of decision trees: a multi-criteria approach
Computers and Operations Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Dimensionality Reduction Techniques for Text Retrieval
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Recommendation method for extending subscription periods
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
The class imbalance problem: A systematic study
Intelligent Data Analysis
Machine learning based adaptive watermark decoding in view of anticipated attack
Pattern Recognition
Customer churn prediction using improved balanced random forests
Expert Systems with Applications: An International Journal
Cluster-based under-sampling approaches for imbalanced data distributions
Expert Systems with Applications: An International Journal
Customer Churn Prediction for Broadband Internet Services
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
An empirical comparison of repetitive undersampling techniques
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Churn prediction in telecom using a hybrid two-phase feature selection method
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease
Computers and Electrical Engineering
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines
Expert Systems with Applications: An International Journal
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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.