A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data

2012 ◽  
Vol 223 (2) ◽  
pp. 461-472 ◽  
Author(s):  
Zhen-Yu Chen ◽  
Zhi-Ping Fan ◽  
Minghe Sun
Keyword(s):  
Support Vector Machine ◽  
Behavioral Data ◽  
Support Vector ◽  
Churn Prediction ◽  
Customer Churn ◽  
Multiple Kernel ◽  
Kernel Support Vector Machine ◽  
Customer Churn Prediction

Multiple Kernel Support Vector Machine Problem Is NP-Complete

2014 ◽  
pp. 152-159 ◽  
Author(s):  
Luis Carlos Padierna ◽  
Juan Martín Carpio ◽  
María del Rosario Baltazar ◽  
Héctor José Puga ◽  
Héctor Joaquín Fraire
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Multiple Kernel ◽  
Kernel Support Vector Machine ◽  
Np Complete ◽  
Machine Problem

scholarly journals Optimal customer relationship management in telecalling industry by using data mining and business intelligence

2017 ◽  
Vol 7 (1.1) ◽  
pp. 12
Author(s):  
T. Kamalakannan ◽  
P. Mayilvaghnan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Business Intelligence ◽  
Research Method ◽  
Support Vector ◽  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
Positive Rate ◽  
Customer Churn Prediction

Decision making system in telecommunication industries plays a more important role where it is required to find customer churn. Customer churn prediction requires finding out and analyzing the information about the business data intelligence techniques which can be done efficiently by adapting the business intelligence techniques. Business intelligence provides tools to predict and analyze the historical, current and predictive views of business operations. However, this would be more complex task with high volume of data which are gathered from million of telephone users for the time being. It can be handled effectively by introducing the data mining techniques which select the most useful information from the gathered data set from which decision making can be done efficiently. In this research method, telecommunication industry is considered in which customer churn prediction application is focused. The main goal of this research method is to introduce the data mining technique which can select the most useful information from the telecommunication industry dataset. This is done by introducing the Hybrid Genetic Algorithm with Particle Swarm Optimization (HGAPSO) method which can select the most useful information. In this research, the hybrid HGAPSO combines the advantages of PSO and GA optimally. From the selected information, decision making about the customer churn prediction can be done accurately. Finally decision making is done by predicting the customer behaviour using Support Vector Machine classification approach. The performance metrics are considered such as precision, recall, f-measure, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), time complexity and ROC. Experimental results demonstrated that HGAPSO provides highly scalable which is used for prediction examination in the business intelligence.

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