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1860797496077320192
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2024-08-27 15:41:15
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Restricted Document
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12971
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UniSZA
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[1] A. Sharma. 2011. Neural Network based Approach for Predicting Customer Churn in Cellular Network Services. International Journal of Computer Applications. 27(11): 26–31. [2] R. A. Soeini, K. V. Rodpysh. 2012. Applying Data Mining to Insurance Customer Churn Management. IACSIT Hong Kong Conference. 30: 82–92. [3] J. Hadden. 2008. A Customer Profiling Methodology for Churn Prediction.Cranfield University. [4] L. Bin, S. Peiji, L. Juan. 2007. Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service. International Conference Service Systems and Service Management. [5] Y. Zhang, M. Berry. 2011. Behavior-Based Telecommunication Churn Prediction with Neural Network Approach. International Symposium on Computer Sciance and Society. pp. 307–310. [6] C. Kirui, L. Hong, W. Cheruiyot, H. Kirui. 2013. Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining. International Journal of Computer Science. 10(2): 165–172. [7] V. Effendy. 2014. Handling Imbalanced Data in Customer Churn Prediction Using Combined Sampling and Weighted Random Forest. Information and Communication Technology (ICoICT), 2014 2nd International Conference. pp. 325–330. [8] S. Sarkar. 2014. An Improved Rough Set Data Model for Stock Market Prediction. Business and Information Management (ICBIM), 2014 2nd International Conference. pp. 96–100. [9] M. R. Ismail, M. K. Awang, M. N. A. Rahman, M. Makhtar. 2015 Multi-Layer Perceptron Approach for Customer Churn Prediction. International Journal of Multimedia and Ubiquitous Engineering. 10(7): 213– 222. [10] Z. Pawlak. 1996. Rough Sets and Data Analysis. Fuzzy Systems Symposuim. pp. 9–14. [11] R. W. Swiniarski, A. Skowron. 2003. Rough set methods in feature selection and recognition. Pattern Recognition Letters. 24(6): 833–849. [12] M. Khoza, T. Marwala. 2011. A rough set theory based predictive model for stock prices. Computational Intelligence and Informatics (CINTI), 2011 IEEE 12th International Symposium. pp. 57–62. [13] N. Senthilkumaran, R. Rajesh. 2009. A Study on Rough Set Theory for Medical Image Segmentation. International Journal of Recent Trends in Engineering. 2(2): 2–4. [14] M. Anouncia R. T. Nandhini. 2013. Design of a Diabetic Diagnosis System Using Rough Sets P. Cybernetics and Information Technologies. 13(3): 124–139. [15] M. N. A. Rahman, Y. M. Lazim, F. Mohamed. 2011. Applying Rough Set Theory in Multimedia Data Classification. International Journal on New Computer Architectures and Their Applications (IJNCAA). 1(3): 683–693. [16] J.-T. Wong,Y.-S. Chung. 2007. Rough set approach for accident chains exploration. Accident analysis and prevention. 39(3): 629. [17] Z. Pawlak. 1996. Rough sets and data analysis. Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium. 147: 1–12. [18] N. Syafiqah, M. Mokhairi, M. N. A.Rahman, M. D. Mustafa, A. Mohd Khalid. 2015. Feature Selections and Classification Model for Customer Churn. Journal of Theoretical and Applied Information Technology. 75(3): 356–365.
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12971 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12971 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 6 1.6 Adobe Acrobat Pro DC 20.6.20042 2024-08-27 15:41:15 7278-01-FH02-FIK-16-05877.pdf UniSZA Private Access Predictive Modeling for Telco Customer Churn using Rough Set Theory ARPN Journal of Engineering and Applied Sciences A rough set is a mathematical tool to handle imprecise and imperfect information. It has been increasing in popularity recently in Knowledge Discovery in Database (KDD) and Machine Learning application. Rough set is one of the techniques used in KDD data mining. Data mining is an approach to extract useful information from a massive database for business purposes, for example, classifying customer churn. Churn is customer behaviour to terminate a service in favour of a competitor. Identifying customers who are likely to churn in the early stage will help firms to increase profitability since acquiring new customers is costly compared to retaining existing one. Limited research in investigating customer churn using machine learning techniques had led this research to discover the potential of rough set theory to enhance customer churn classification. This paper proposes a rough set predictive classification framework for customer churn in Telecommunication Companies. Experimental results show that the classification model is able to classify up to 83% to 98% accuracy for customer churn dataset. Overall, this indicates that the rough set theory is effective to classify customer churn compared to traditional statistical predictive approaches. 11 5 Asian Research Publishing Network Asian Research Publishing Network 3203-3207 [1] A. Sharma. 2011. Neural Network based Approach for Predicting Customer Churn in Cellular Network Services. International Journal of Computer Applications. 27(11): 26–31. [2] R. A. Soeini, K. V. Rodpysh. 2012. Applying Data Mining to Insurance Customer Churn Management. IACSIT Hong Kong Conference. 30: 82–92. [3] J. Hadden. 2008. A Customer Profiling Methodology for Churn Prediction.Cranfield University. [4] L. Bin, S. Peiji, L. Juan. 2007. Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service. International Conference Service Systems and Service Management. [5] Y. Zhang, M. Berry. 2011. Behavior-Based Telecommunication Churn Prediction with Neural Network Approach. International Symposium on Computer Sciance and Society. pp. 307–310. [6] C. Kirui, L. Hong, W. Cheruiyot, H. Kirui. 2013. Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining. International Journal of Computer Science. 10(2): 165–172. [7] V. Effendy. 2014. Handling Imbalanced Data in Customer Churn Prediction Using Combined Sampling and Weighted Random Forest. Information and Communication Technology (ICoICT), 2014 2nd International Conference. pp. 325–330. [8] S. Sarkar. 2014. An Improved Rough Set Data Model for Stock Market Prediction. Business and Information Management (ICBIM), 2014 2nd International Conference. pp. 96–100. [9] M. R. Ismail, M. K. Awang, M. N. A. Rahman, M. Makhtar. 2015 Multi-Layer Perceptron Approach for Customer Churn Prediction. International Journal of Multimedia and Ubiquitous Engineering. 10(7): 213– 222. [10] Z. Pawlak. 1996. Rough Sets and Data Analysis. Fuzzy Systems Symposuim. pp. 9–14. [11] R. W. Swiniarski, A. Skowron. 2003. Rough set methods in feature selection and recognition. Pattern Recognition Letters. 24(6): 833–849. [12] M. Khoza, T. Marwala. 2011. A rough set theory based predictive model for stock prices. Computational Intelligence and Informatics (CINTI), 2011 IEEE 12th International Symposium. pp. 57–62. [13] N. Senthilkumaran, R. Rajesh. 2009. A Study on Rough Set Theory for Medical Image Segmentation. International Journal of Recent Trends in Engineering. 2(2): 2–4. [14] M. Anouncia R. T. Nandhini. 2013. Design of a Diabetic Diagnosis System Using Rough Sets P. Cybernetics and Information Technologies. 13(3): 124–139. [15] M. N. A. Rahman, Y. M. Lazim, F. Mohamed. 2011. Applying Rough Set Theory in Multimedia Data Classification. International Journal on New Computer Architectures and Their Applications (IJNCAA). 1(3): 683–693. [16] J.-T. Wong,Y.-S. Chung. 2007. Rough set approach for accident chains exploration. Accident analysis and prevention. 39(3): 629. [17] Z. Pawlak. 1996. Rough sets and data analysis. Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium. 147: 1–12. [18] N. Syafiqah, M. Mokhairi, M. N. A.Rahman, M. D. Mustafa, A. Mohd Khalid. 2015. Feature Selections and Classification Model for Customer Churn. Journal of Theoretical and Applied Information Technology. 75(3): 356–365.
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| spellingShingle |
Predictive Modeling for Telco Customer Churn using Rough Set Theory
|
| summary |
A rough set is a mathematical tool to handle imprecise and imperfect information. It has been increasing in popularity recently in Knowledge Discovery in Database (KDD) and Machine Learning application. Rough set is one of the techniques used in KDD data mining. Data mining is an approach to extract useful information from a massive database for business purposes, for example, classifying customer churn. Churn is customer behaviour to terminate a service in favour of a competitor. Identifying customers who are likely to churn in the early stage will help firms to increase profitability since acquiring new customers is costly compared to retaining existing one. Limited research in investigating customer churn using machine learning techniques had led this research to discover the potential of rough set theory to enhance customer churn classification. This paper proposes a rough set predictive classification framework for customer churn in Telecommunication Companies. Experimental results show that the classification model is able to classify up to 83% to 98% accuracy for customer churn dataset. Overall, this indicates that the rough set theory is effective to classify customer churn compared to traditional statistical predictive approaches.
|
| title |
Predictive Modeling for Telco Customer Churn using Rough Set Theory
|
| title_full |
Predictive Modeling for Telco Customer Churn using Rough Set Theory
|
| title_fullStr |
Predictive Modeling for Telco Customer Churn using Rough Set Theory
|
| title_full_unstemmed |
Predictive Modeling for Telco Customer Churn using Rough Set Theory
|
| title_short |
Predictive Modeling for Telco Customer Churn using Rough Set Theory
|
| title_sort |
predictive modeling for telco customer churn using rough set theory
|