Genetic Programming and Adaboosting based churn prediction for Telecom

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Idris:2012:SMC,

• author = "Adnan Idris and Asifullah Khan and Yeon Soo Lee",
• booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2012)",
• title = "Genetic Programming and Adaboosting based churn prediction for Telecom",
• year = "2012",
• pages = "1328--1332",
• month = oct # " 14-17",
• address = "Seoul, Korea",
• DOI = "doi:10.1109/ICSMC.2012.6377917",
• size = "5 pages",
• abstract = "Churn prediction model guides the customer relationship management to retain the customers who are expected to quit. In recent times, a number of tree based ensemble classifiers are used to model the churn prediction in telecom. These models predict the churners quite satisfactorily; however, there is a considerable margin of improvement. In telecom, the enormous size, imbalanced nature, and high dimensionality of the training dataset mainly cause the classification algorithms to suffer in accurately predicting the churners. In this paper, we use Genetic Programming (GP) based approach for modelling the challenging problem of churn prediction in telecom. Adaboost style boosting is used to evolve a number of programs per class. Finally, the predictions are made with the resulting programs using the higher output, from a weighted sum of the outputs of programs per class. The prediction accuracy is evaluated using 10 fold cross validation on standard telecom datasets and a 0.89 score of area under the curve is observed. We hope that such an efficient churn prediction approach might be significantly beneficial for the competitive telecom industry.",
• keywords = "genetic algorithms, genetic programming, customer relationship management, learning (artificial intelligence), pattern classification, telecommunication computing, telecommunication industry, trees (mathematics), GP based approach, adaboosting based churn prediction, churn prediction model, classification algorithms, customer relationship management, prediction accuracy, telecom datasets, telecom industry, training dataset, tree based ensemble classifiers, Accuracy, Boosting, Prediction algorithms, Predictive models, Sociology, Telecommunications, Training, Adaboost, churn prediction, cross validation, prediction accuracy, telecom",
• notes = "Also known as \cite{6377917}",

}

Genetic Programming entries for Adnan Idris Asifullah Khan Yeon Soo Lee

Citations