Genetic Programming and Adaboosting based churn prediction for Telecom

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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