Predictive Modeling for Telco Customer Churn using Rough Set Theory

Nafis, Syafiqah and Makhtar, Mokhairi and Awang, Mohd Khalid and Abd Rahman, Mohd Nordin and Mat Deris, Mustafa (2015) Predictive Modeling for Telco Customer Churn using Rough Set Theory. ARPN Journal of Engineering and Applied Sciences, 11 (5). pp. 3203-3207. ISSN 1819-6608

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Rough set a mathematical tool to handle imprecise and imperfect information. It has been increased in popularity recently in Knowledge Discovery and Machine Learning application. One of the common steps in knowledge discovery which used rough set is data mining. Data mining is an approach to extract useful information from massive database. This useful knowledge can be beneficial for business purposes for example predicting customer churn. Churn is customer behaviour to terminate a service for competitor. This paper propose rough set classification model for customer churn in Telecommunication Company. Experimental results show that classification model able to classify up to 83% to 98% accuracy for customer churn dataset. This indicates that the proposed model is effective to classify customer churn.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Institute: Faculty of Informatics & Computing
Depositing User: Mokhairi Makhtar
Date Deposited: 11 Aug 2016 07:34
Last Modified: 11 Aug 2016 07:34

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