Intelligent Churn prediction for Telecommunication Industry

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Khan:2013:IJIAS,
  author =       "Imran Khan and Imran Usman and Tariq Usman and 
                 Ghani {Ur Rehman} and Ateeq {Ur Rehman}",
  title =        "Intelligent Churn prediction for Telecommunication
                 Industry",
  journal =      "International Journal of Innovation and Applied
                 Studies",
  year =         "2013",
  volume =       "4",
  number =       "1",
  pages =        "165--170",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, churn
                 prediction, artificial neural networks, support vector
                 machines, broadband networks",
  ISSN =         "2028-9324",
  bibsource =    "OAI-PMH server at www.doaj.org",
  language =     "eng",
  oai =          "oai:doaj-articles:d799bb14026e2f5e358ba84726ba5a9b",
  publisher =    "ISSR Journals",
  URL =          "http://www.issr-journals.org/xplore/ijias/IJIAS-13-147-13.pdf",
  abstract =     "Customer churn is a focal concern for most of the
                 services based companies which have fixed operating
                 costs. Among various industries which suffer from this
                 issue, telecommunications industry can be considered at
                 the top of the list. In order to counter this problem
                 one must recognise the churners before they churn. This
                 work develops an effective and efficient model which
                 has the ability to predict the future churners for
                 broadband Internet services. For this purpose Genetic
                 Programming (GP) is employed to evolve a suitable
                 classifier by using the customer based features.
                 Genetic Programming (GP) is population based heuristic
                 used to solve complex multimodal optimisation problems.
                 It is an evolutionary approach use the Darwinian
                 principle of natural selection (survival of the
                 fittest) analogs with various naturally occurring
                 operations, including crossover (sexual recombination),
                 mutation (to randomly perturbed or change the
                 respective gene value) and gene duplication. The
                 intelligence induced in the system not only generalises
                 the model for a variety of real world applications but
                 also make it adaptable for dynamic environment.
                 Comprehensive experimentations are performed in order
                 to validate the effectiveness and robustness of the
                 proposed system. It is clear from the experimental
                 results that the proposed system outperforms other
                 state of the art churn prediction techniques.",
}

Genetic Programming entries for Imran Khan Imran Usman Tariq Usman Ghani-Ur-Rehman Ateeq-Ur-Rehman

Citations