Title: A study of feature selection techniques for predicting customer retention in telecommunication sector
Authors: E. Sivasankar; J. Vijaya
Addresses: Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu, India ' Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu, India
Abstract: Feature selection is the process of eliminating irrelevant features from the dataset, while maintaining acceptable classification accuracy. The selected features play an important role which can directly influence the effectiveness of the resulting classification. In this paper, a methodology is proposed consisting of two phases, attributes selection and classification based on the attributes selected. Phase one uses a filter and wrapper method for attribute selection with random over-sampling (Ros) through which the size of attributes set and misclassification error can be reduced. In the second phase, the selected attributes are taken as inputs by classification techniques like decision trees (DT), K-nearest neighbour (KNN), support vector machine (SVM), naive Bayes (NB) and artificial neural network (ANN). Finally, true churn, false churn, specificity and accuracy are measured to evaluate the efficiency of the proposed system and it is found that the above mentioned methodology performs well ahead for churn prediction and suits well for the telecommunication sector.
Keywords: churn prediction; random over sampling; feature selection; filter method; wrapper method; decision trees; k-nearest neighbour; KNN; support vector machine; SVM; naive Bayes; artificial neural network; ANN.
International Journal of Business Information Systems, 2019 Vol.31 No.1, pp.1 - 26
Received: 25 May 2017
Accepted: 03 Sep 2017
Published online: 08 May 2019 *