An ensemble method with cost function on churn prediction
Accurate customer churn classification is vital in any business organisation due to the 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 relat...
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Main Authors: | , , |
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Format: | Conference or Workshop Item |
Language: | English English |
Published: |
2019
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Subjects: | |
Online Access: | http://eprints.unisza.edu.my/1874/1/FH03-FIK-20-36862.pdf http://eprints.unisza.edu.my/1874/2/FH03-FIK-20-36863.pdf http://eprints.unisza.edu.my/1874/ |
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Institution: | Universiti Sultan Zainal Abidin |
Language: | English English |
Summary: | Accurate customer churn classification is vital in any business organisation due to the 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
to produce higher classification accuracy, the ensemble method tends to produce similar or redundant classifiers.
Therefore, this paper aims to achieve higher classification accuracy and at the same time, minimising ensemble
classifiers by constructing a new ensemble method based on dimensionality reduction in soft set theory. The
combination of ensemble classifier is calculated based on the simple majority voting algorithm. The performance
measure used in determining the optimal subset of classifiers is the combination of Accuracy (ACC), True Negative
Rate (TNR) and True Positive Rate (TPR). The proposed soft set ensemble methods (SSPN and SSSC) are systematically
evaluated using customer churn data set taken from one of the local Telco companies in Malaysia. The selection and
combination algorithm (SSSC) has proven its supremacy by producing accuracy (ACC) of 87.0% for local Telco data set
and 94.0% for UCI data set, which is better than any other single classifier. This work proved that the proposed soft
ensemble method could search for the minimum number of classifiers in the ensemble repository while at the same
time improving the classification performance. In conclusion, the proposed soft ensemble method not only reduces
the number of members of the ensemble but is also able to produce the highest classification accuracy |
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