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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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2024-08-30 11:21:30
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15235
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UniSZA
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[1] J. Lu and D. Ph, “Predicting Customer Churn in the Telecommunications Industry –– An Application of Survival Analysis Modeling Using SAS â,” 2011. [2] J. Hadden, A. Tiwari, R. Roy, and D. Ruta, “Churn Prediction using Complaints Data,” no. 1999, 2006. [3] A. Sharma, “A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services,” vol. 27, no. 11, pp. 26–31, 2011. [4] C.-F. Tsai and M.-Y. Chen, “Variable selection by association rules for customer churn prediction of multimedia on demand,” Expert Syst. Appl., vol. 37, no. 3, pp. 2006–2015, Mar. 2010. [5] R. A. Soeini and K. V. Rodpysh, “Applying Data Mining to Insurance Customer Churn Management,” vol. 30, pp. 82–92, 2012. [6] N. A. Haris, O. Support, and S. Division, “Data Mining in Churn Analysis Model for Telecommunication Industry,” vol. 1, no. 19, pp. 19–27, 2010. [7] S. Nabavi and S. Jafari, “Providing a Customer Churn Prediction Model Using Random Forest and Boosted Trees Techniques (Case Study: Solico Food Industries Group),” vol. 3, no. 6, pp. 1018– 1026, 2013. [8] R. J. Jadhav, “Churn Prediction in Telecommunication Using Data Mining Technology,” vol. 2, no. 2, pp. 17–19, 2011. [9] C. Kirui, L. Hong, W. Cheruiyot, and H. Kirui, “Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining,” vol. 10, no. 2, pp. 165–172, 2013. [10] S. V Nath, “Customer Churn Analysis in the Wireless Industry: A Data Mining Approach Customer Churn Analysis in the Wireless Industry: A Data Mining Approach,” no. 561, pp. 1–20, 2003. [11] A. J. Dawson, H. Stasa, M. a Roche, C. S. E. Homer, and C. Duffield, “Nursing churn and turnover in Australian hospitals: Nurses perceptions and suggestions for supportive strategies.,” BMC Nurs., vol. 13, p. 11, 2014. [12] U. D. Prasad, “Prediction of Churn Behavior of Bank Customers,” Bus. Intell. J., vol. 5, pp. 96–101, 2012. [13] A. An, “Classification Methods,” pp. 144– 149, Jun. 2005. [14] M. N. A. Rahman, Y. M. Lazim, and F. Mohamed, “Applying Rough Set Theory in Multimedia Data Classification,” vol. 1, no. 3, pp. 683–693, 2011. [15] Z. Z. Z. Zhu, “An Email Classification Model Based on Rough Set and Support Vector Machine,” 2008 Fifth Int. Conf. Fuzzy Syst. Knowl. Discov., vol. 5, pp. 403– 408, 2008. [16] N. S. Kamarudin, M. Makhtar, S. A. Fadzli, M. Mohamad, F. S. Mohamad, M. F. Abdul Kadir, “Comparison of Image Classification Techniques Using Caltech 101 Dataset,” vol. 71, no. 1, 2015. [17] Q. A. Al-radaideh, “The Impact of Classification Evaluation Methods on Rough Set Based Classifier,” no. 1, pp. 2–6, 2008. [18] L. Ladha and T. Deepa, “Feature Selection Methods and Algorithms,” Int. J. Comput. Sci. Eng., vol. 3, pp. 1787–1797, 2011. [19] P. Ozer, “Data Mining Algorithms for Classification,” no. January, 2008. [20] A. A. A. Hafieza Ismail, Fadhilah Ahmad, “Seminar Penyelidikan Siswazah UniSZA Peringkat Kebangsaan (SEMPSIS), Implementing WEKA as a Data Mining Tool to Analyze Students’ Academic Performances using Naïve Bayes Classifier Nur Hafieza Ismail, Fadhilah Ahmad, Azwa Abdul Aziz University Sultan,” no. July 2011, 2013. [21] M. A. Hall and I. H. Witten, “WEKA — Experiences with a Java Open-Source Project,” J. Mach. Learn. Res., vol. 11, pp. 2533–2541, 2010. [22] A. M. Taha, A. Mustapha, and S. Der Chen, “Naive Bayes-guided bat algorithm for feature selection,” Sci. World J., vol. 2013, 2013. [23] D. R. Chowdhury, M. Chatterjee, and R. K. Samanta, “An Artificial Neural Network Model for Neonatal Disease Diagnosis,” Int. J. Artif. Intell. Expert Syst., vol. 2, pp. 96–106, 2011.
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15235 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15235 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 11 1.6 123 2024-08-30 11:21:30 6027-01-FH02-FIK-15-03308.pdf UniSZA Private Access Feature Selections and Classification Model for Customer Churn Journal of Theoretical and Applied Information Technology 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. 75 3 Asian Research Publishing Network Asian Research Publishing Network 356-365 [1] J. Lu and D. Ph, “Predicting Customer Churn in the Telecommunications Industry –– An Application of Survival Analysis Modeling Using SAS â,” 2011. [2] J. Hadden, A. Tiwari, R. Roy, and D. Ruta, “Churn Prediction using Complaints Data,” no. 1999, 2006. [3] A. Sharma, “A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services,” vol. 27, no. 11, pp. 26–31, 2011. [4] C.-F. Tsai and M.-Y. Chen, “Variable selection by association rules for customer churn prediction of multimedia on demand,” Expert Syst. Appl., vol. 37, no. 3, pp. 2006–2015, Mar. 2010. [5] R. A. Soeini and K. V. Rodpysh, “Applying Data Mining to Insurance Customer Churn Management,” vol. 30, pp. 82–92, 2012. [6] N. A. Haris, O. Support, and S. Division, “Data Mining in Churn Analysis Model for Telecommunication Industry,” vol. 1, no. 19, pp. 19–27, 2010. [7] S. Nabavi and S. Jafari, “Providing a Customer Churn Prediction Model Using Random Forest and Boosted Trees Techniques (Case Study: Solico Food Industries Group),” vol. 3, no. 6, pp. 1018– 1026, 2013. [8] R. J. Jadhav, “Churn Prediction in Telecommunication Using Data Mining Technology,” vol. 2, no. 2, pp. 17–19, 2011. [9] C. Kirui, L. Hong, W. Cheruiyot, and H. Kirui, “Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining,” vol. 10, no. 2, pp. 165–172, 2013. [10] S. V Nath, “Customer Churn Analysis in the Wireless Industry: A Data Mining Approach Customer Churn Analysis in the Wireless Industry: A Data Mining Approach,” no. 561, pp. 1–20, 2003. [11] A. J. Dawson, H. Stasa, M. a Roche, C. S. E. Homer, and C. Duffield, “Nursing churn and turnover in Australian hospitals: Nurses perceptions and suggestions for supportive strategies.,” BMC Nurs., vol. 13, p. 11, 2014. [12] U. D. Prasad, “Prediction of Churn Behavior of Bank Customers,” Bus. Intell. J., vol. 5, pp. 96–101, 2012. [13] A. An, “Classification Methods,” pp. 144– 149, Jun. 2005. [14] M. N. A. Rahman, Y. M. Lazim, and F. Mohamed, “Applying Rough Set Theory in Multimedia Data Classification,” vol. 1, no. 3, pp. 683–693, 2011. [15] Z. Z. Z. Zhu, “An Email Classification Model Based on Rough Set and Support Vector Machine,” 2008 Fifth Int. Conf. Fuzzy Syst. Knowl. Discov., vol. 5, pp. 403– 408, 2008. [16] N. S. Kamarudin, M. Makhtar, S. A. Fadzli, M. Mohamad, F. S. Mohamad, M. F. Abdul Kadir, “Comparison of Image Classification Techniques Using Caltech 101 Dataset,” vol. 71, no. 1, 2015. [17] Q. A. Al-radaideh, “The Impact of Classification Evaluation Methods on Rough Set Based Classifier,” no. 1, pp. 2–6, 2008. [18] L. Ladha and T. Deepa, “Feature Selection Methods and Algorithms,” Int. J. Comput. Sci. Eng., vol. 3, pp. 1787–1797, 2011. [19] P. Ozer, “Data Mining Algorithms for Classification,” no. January, 2008. [20] A. A. A. Hafieza Ismail, Fadhilah Ahmad, “Seminar Penyelidikan Siswazah UniSZA Peringkat Kebangsaan (SEMPSIS), Implementing WEKA as a Data Mining Tool to Analyze Students’ Academic Performances using Naïve Bayes Classifier Nur Hafieza Ismail, Fadhilah Ahmad, Azwa Abdul Aziz University Sultan,” no. July 2011, 2013. [21] M. A. Hall and I. H. Witten, “WEKA — Experiences with a Java Open-Source Project,” J. Mach. Learn. Res., vol. 11, pp. 2533–2541, 2010. [22] A. M. Taha, A. Mustapha, and S. Der Chen, “Naive Bayes-guided bat algorithm for feature selection,” Sci. World J., vol. 2013, 2013. [23] D. R. Chowdhury, M. Chatterjee, and R. K. Samanta, “An Artificial Neural Network Model for Neonatal Disease Diagnosis,” Int. J. Artif. Intell. Expert Syst., vol. 2, pp. 96–106, 2011.
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| spellingShingle |
Feature Selections and Classification Model for Customer Churn
|
| summary |
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.
|
| title |
Feature Selections and Classification Model for Customer Churn
|
| title_full |
Feature Selections and Classification Model for Customer Churn
|
| title_fullStr |
Feature Selections and Classification Model for Customer Churn
|
| title_full_unstemmed |
Feature Selections and Classification Model for Customer Churn
|
| title_short |
Feature Selections and Classification Model for Customer Churn
|
| title_sort |
feature selections and classification model for customer churn
|