Affiliations:
School of Computer Science and Engineering, Bharathidasan University, Tiruchirapalli, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. T.S. Poornappriya, School of Computer Science and Engineering, Bharathidasan University, Tiruchirapalli, Tamil Nadu, India. E-mail: poorna.priya23@gmail.com.
Abstract: The prompt enhancement of Telecom turned to be a vibrant and economical industry, which comprises an intrinsically great perspective for customer churn, requiring exact churn prediction models. In recent times, there has been phenomenal responsiveness in the development of feature selection methods for a large number of datasets. Through this research work, a High Relevancy and Low Redundancy (HRLR) approach by consuming Vague Set (VS) has proposed for selecting the subset of features from the features set. This proposed method is based on the Minimum Redundancy and Maximum Relevancy (MRMR) approach by using Vague Set. The proposed HRLR-VS method is based on the filtered approach feature selection, where the features are selected only when the measure of feature-class relevancy is maximized and a measure of feature-feature redundancy is minimized. The collaboration of similarity measures and ranking algorithms are prepared by utilizing the vital notions of Vague Sets information energies by Information Gain, Gain Ratio, and Chi-Square methods. The projected approach has been employed with the Particle Swarm Optimization for probing the best feature subset. Further, it measures the efficacy of the projected approach HRHL-VS for telecommunication dataset. The performance metrics like Accuracy, Kappa Statistics, True Positive Rate, Precision, F-Measure, Recall, MAE, RRSE, RMSE and RAE are considered in this paper for evaluating the proposed HRLR-VS method. The proposed HRRL-VS method has compared with existing literature approaches like mRMR and FCBF. From the result obtained in this paper, the proposed HRLR-VS method better results in all aspects for selecting the feature subset in telecommunication dataset.
Keywords: Feature Selection, Vague Set, Information Gain, Gain Ratio, Chi-Square, Particle Swarm Optimization, Euclidean Distance, Cosine Similarity, Pearson’s Correlation Coefficient
