Performance Comparison of Neural Network Training Algorithms for Modeling Customer Churn Prediction

  • Authors

    • Mohd Khalid Awang
    • Mohammad Ridwan Ismail
    • Mokhairi Makhtar
    • M Nordin A Rahman
    • Abd Rasid Mamat
    2018-04-06
    https://doi.org/10.14419/ijet.v7i2.15.11196
  • Neural Network Learning Algorithm, Data Mining, Customer Churn Prediction, Multilayer Perceptron

  • Abstract

    Predicting customer churn has become the priority of every telecommunication service provider as the market  is becoming more saturated and competitive. This paper presents a comparison of neural network learning algorithms for customer churn prediction.  The data set used to train and test the neural network algorithms was provided by one of the leading telecommunication company in Malaysia. The Multilayer Perceptron (MLP) networks are trained using nine (9) types of learning algorithms, which are Levenberg Marquardt backpropagation (trainlm), BFGS Quasi-Newton backpropagation (trainbfg), Conjugate Gradient backpropagation with Fletcher-Reeves Updates (traincgf), Conjugate Gradient backpropagation with Polak-Ribiere Updates (traincgp), Conjugate Gradient backpropagation with Powell-Beale Restarts (traincgb), Scaled Conjugate Gradient backpropagation (trainscg), One Step Secant backpropagation (trainoss), Bayesian Regularization backpropagation (trainbr), and Resilient backpropagation (trainrp). The performance of the Neural Network is measured based on the prediction accuracy of the learning and testing phases. LM learning algorithm is found to be the optimum model of a neural network model consisting of fourteen input units, one hidden node and one output node. The best result of the experiment indicated that this model is able to produce the performance accuracy of 94.82%.

     

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  • How to Cite

    Khalid Awang, M., Ridwan Ismail, M., Makhtar, M., Nordin A Rahman, M., & Rasid Mamat, A. (2018). Performance Comparison of Neural Network Training Algorithms for Modeling Customer Churn Prediction. International Journal of Engineering & Technology, 7(2.15), 35-37. https://doi.org/10.14419/ijet.v7i2.15.11196

    Received date: 2018-04-06

    Accepted date: 2018-04-06

    Published date: 2018-04-06