Feature Selections and Classification Model for Customer Churn

Mokhairi, Makhtar and Mohd Khalid, Awang and Mohd Nordin, Abdul Rahman (2015) Feature Selections and Classification Model for Customer Churn. Journal of Theoretical and Applied Information Technology, 75 (3). pp. 356-365. ISSN 19928645 [P]

[img] Text
Restricted to Registered users only

Download (622kB)
[img] Image
Restricted to Registered users only

Download (154kB)


As customers actively exercise their right to change to a better service and since engaging new customers is more costly compared to retaining loyal customers, customer churn has become the main focus for one organization. This phenomenon affects many industries such as telecommunication companies which need to provide excellent service in order to win over the competition. Several models were developed in previous research using various methods such as the conventional statistical method, decision tree based model and neural network based approach in predicting customer churn. Several experiments were conducted in this research for feature selection and classification from selected customer churn dataset to compare its usefulness among the different feature selections and classifications using a data mining tool. The results from the experiments showed that the Logistic Model Tree (LMT) method is the best method for this dataset with a 95% accuracy enhanced using neural network from previous research.

Item Type: Article
Uncontrolled Keywords: Customer Churn, Classification, Feature Selection, Telecommunications, Data Mining
Subjects: H Social Sciences > HB Economic Theory
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Informatics & Computing
Depositing User: Syahmi Manaf
Date Deposited: 13 Sep 2022 04:52
Last Modified: 13 Sep 2022 04:52
URI: http://eprints.unisza.edu.my/id/eprint/6027

Actions (login required)

View Item View Item