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Efficient Customer Churn Prediction Model Using Support Vector Machine with Particle Swarm Optimization
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kamalakannan, T. |
| Copyright Year | 2018 |
| Abstract | --Today, telecommunication market all over the world is facing a severe loss of revenue due to fierce competition and loss of potential customers. To keep the competitive advantages and acquire as many customers as possible, most operators invest a huge amount of revenue to expand their business in the very beginning. Customer Relationship Management (CRM) strategy helps an organization to improve the business processes and technology solutions around selling, marketing, and servicing functions across all customer touch-points. One of the important issues in customer relationship management is churn prediction. It aims at identifying potential churning customers based on past information and prior behaviors. In this proposed work the data mining techniques were utilized for efficient churn prediction. Here the normalized k means algorithm is utilized for dataset preprocessing. Then the attributes are selected from preprocessed image by utilizing minimum Redundancy and Maximum Relevance (mRMR) approach. It tends to select attributes with a high correlation with the class (output) and a low correlation between themselves. Based on the selected attributes the customer churn separation or prediction is examined with the help of Support Vector Machine with Particle Swarm Optimization (SVM with PSO). In order to optimize the hyper parameters of SVM the PSO is used. And also it overcome the local optimal solution problem and obtains higher classification accuracy. The experimental results show that the proposed system achieves better performance compared with the existing system in terms of accuracy, true positive rate, false positive rate and processing time. Keywords---Customer Relationship Management (CRM), Particle Swarm Optimization (PSO), Churn Prediction and Support Vector Machine (SVM). |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://acadpubl.eu/jsi/2018-119-10/articles/10b/99.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
