An Efficient Churn Mining Using Particle Swarm Based Boosted Tree

Title: An Efficient Churn Mining Using Particle Swarm Based Boosted Tree
Publisher: Guru Nanak Publications
ISSN: 2278-0947
Series: Volume 5 Issue 2
Authors: Sarbinder Pal Singh, Kiranbir Kaur, Sandeep Sharma


Churn Prediction has been major research problem with the growth of market development as customers asset more valuable persons for growth of company. The occurrence of churn customers is one of the crucial problems for the growth of a company, as it acquires higher costs. The task of churn prediction is to identify the customers who are pretending to shift from one company to another. As in the competitive environment, it becomes necessary to focus on retaining churn customers as well as attracting new customers. Various algorithms of Data Mining have been used for making distinguish between customers into loyal and churn, so that appropriate steps can be taken into consideration in order to retain the churn customers to the company as customers are more valuable to the survival and development of the company. The proposed Hybrid approach is an integration of two techniques named J48 and LogitBoost that have feature of PSO (Particle Swarm Optimization), provides better and accurate results in the prediction of churn customers. PSO is used to search the best solution with two best values named pbst and gbst by using iteration with initial velocity and positions. The experiment results reveal good distinction of churn and loyal customers from the given dataset and provide more accurate and satisfactory results when the Hybrid approach is compared with various classifiers or algorithms.


Boost Tree, Churm Prediction, Data Mining, J48, Particle Swarm Optimization.

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