Feature Selections and Classification Model for Customer Churn
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 w...
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Asian Research Publishing Network
2015
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my-unisza-ir.60272022-09-13T04:52:50Z http://eprints.unisza.edu.my/6027/ Feature Selections and Classification Model for Customer Churn Mokhairi, Makhtar Mohd Khalid, Awang Mohd Nordin, Abdul Rahman HB Economic Theory QA75 Electronic computers. Computer science 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. Asian Research Publishing Network 2015-05 Article PeerReviewed text en http://eprints.unisza.edu.my/6027/1/FH02-FIK-15-03308.pdf image en http://eprints.unisza.edu.my/6027/2/FH02-FIK-15-03462.jpg 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] |
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HB Economic Theory QA75 Electronic computers. Computer science Mokhairi, Makhtar Mohd Khalid, Awang Mohd Nordin, Abdul Rahman Feature Selections and Classification Model for Customer Churn |
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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. |
format |
Article |
author |
Mokhairi, Makhtar Mohd Khalid, Awang Mohd Nordin, Abdul Rahman |
author_facet |
Mokhairi, Makhtar Mohd Khalid, Awang Mohd Nordin, Abdul Rahman |
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Mokhairi, Makhtar |
title |
Feature Selections and Classification Model for Customer Churn |
title_short |
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_sort |
feature selections and classification model for customer churn |
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Asian Research Publishing Network |
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2015 |
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http://eprints.unisza.edu.my/6027/1/FH02-FIK-15-03308.pdf http://eprints.unisza.edu.my/6027/2/FH02-FIK-15-03462.jpg http://eprints.unisza.edu.my/6027/ |
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