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1860799670382493696
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2017-02-02 11:49:36
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| eventvenue |
Bandung, Indonesia
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| format |
Restricted Document
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| id |
6930
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| institution |
UniSZA
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| originalfilename |
1689-01-FH03-FIK-17-08102.jpg
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| person |
norman
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| recordtype |
oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6930
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| spelling |
6930 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6930 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper image/jpeg inches 96 96 norman 1439 772 77 77 1439x772 2017-02-02 11:49:36 1689-01-FH03-FIK-17-08102.jpg UniSZA Private Access A new customer churn prediction approach based on soft set ensemble pruning Accurate customer churn prediction is vital in any business organization due to higher cost involved in getting new customers. In telecommunication businesses, companies have used various types of single classifiers to classify customer churn, but the classification accuracy is still relatively low. However, the classification accuracy can be improved by integrating decisions from multiple classifiers through an ensemble method. Despite having the ability of producing the highest classification accuracy, ensemble methods have suffered significantly from their large volume of base classifiers. Thus, in the previous work, we have proposed a novel soft set based method to prune the classifiers from heterogeneous ensemble committee and select the best subsets of the component classifiers prior to the combination process. The results of the previous study demonstrated the ability of our proposed soft set ensemble pruning to reduce a substantial number of classifiers and at the same time producing the highest prediction accuracy. In this paper, we extended our soft set ensemble pruning on the customer churn dataset. The results of this work have proven that our proposed method of soft set ensemble pruning is able to overcome one of the drawbacks of ensemble method. Ensemble pruning based on soft set theory not only reduce the number of members of the ensemble, but able to increase the prediction accuracy of customer churn. The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016; Bandung, Indonesia
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| spellingShingle |
A new customer churn prediction approach based on soft set ensemble pruning
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| summary |
Accurate customer churn prediction is vital in any business organization due to higher cost involved in getting new customers. In telecommunication businesses, companies have used various types of single classifiers to classify customer churn, but the classification accuracy is still relatively low. However, the classification accuracy can be improved by integrating decisions from multiple classifiers through an ensemble method. Despite having the ability of producing the highest classification accuracy, ensemble methods have suffered significantly from their large volume of base classifiers. Thus, in the previous work, we have proposed a novel soft set based method to prune the classifiers from heterogeneous ensemble committee and select the best subsets of the component classifiers prior to the combination process. The results of the previous study demonstrated the ability of our proposed soft set ensemble pruning to reduce a substantial number of classifiers and at the same time producing the highest prediction accuracy. In this paper, we extended our soft set ensemble pruning on the customer churn dataset. The results of this work have proven that our proposed method of soft set ensemble pruning is able to overcome one of the drawbacks of ensemble method. Ensemble pruning based on soft set theory not only reduce the number of members of the ensemble, but able to increase the prediction accuracy of customer churn.
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| title |
A new customer churn prediction approach based on soft set ensemble pruning
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| title_full |
A new customer churn prediction approach based on soft set ensemble pruning
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| title_fullStr |
A new customer churn prediction approach based on soft set ensemble pruning
|
| title_full_unstemmed |
A new customer churn prediction approach based on soft set ensemble pruning
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| title_short |
A new customer churn prediction approach based on soft set ensemble pruning
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| title_sort |
new customer churn prediction approach based on soft set ensemble pruning
|