Genetic Programming and Adaboosting based churn prediction for Telecom
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gp-bibliography.bib Revision:1.8098
- @InProceedings{Idris:2012:SMC,
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author = "Adnan Idris and Asifullah Khan and Yeon Soo Lee",
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booktitle = "IEEE International Conference on Systems, Man, and
Cybernetics (SMC 2012)",
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title = "Genetic Programming and Adaboosting based churn
prediction for Telecom",
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year = "2012",
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pages = "1328--1332",
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month = oct # " 14-17",
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address = "Seoul, Korea",
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DOI = "doi:10.1109/ICSMC.2012.6377917",
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size = "5 pages",
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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.",
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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",
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notes = "Also known as \cite{6377917}",
- }
Genetic Programming entries for
Adnan Idris
Asifullah Khan
Yeon Soo Lee
Citations