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Performance Evaluation of Churn Customer Behavior based on Hybrid Algorithm
by
Riddhima Rikhi Sharma,
Rajan Sachdeva
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 159 - Number 6 |
| Year of Publication: 2017 |
| Authors: Riddhima Rikhi Sharma, Rajan Sachdeva |
10.5120/ijca2017912959
|
Riddhima Rikhi Sharma, Rajan Sachdeva . Performance Evaluation of Churn Customer Behavior based on Hybrid Algorithm. International Journal of Computer Applications. 159, 6 ( Feb 2017), 14-19. DOI=10.5120/ijca2017912959
@article{
10.5120/ijca2017912959,
author = {
Riddhima Rikhi Sharma,
Rajan Sachdeva
},
title = { Performance Evaluation of Churn Customer Behavior based on Hybrid Algorithm },
journal = {
International Journal of Computer Applications
},
issue_date = { Feb 2017 },
volume = { 159 },
number = { 6 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = {
14-19
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume159/number6/27005-2017912959/
},
doi = { 10.5120/ijca2017912959 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:05:02.739110+05:30
%A Riddhima Rikhi Sharma
%A Rajan Sachdeva
%T Performance Evaluation of Churn Customer Behavior based on Hybrid Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 6
%P 14-19
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Various algorithms of Data Mining have been used for making distinguish between customers into loyal and churn. Boosting algorithms are iterative studying process that will combines poor classifiers as a way to create a powerful a classifiers. SVM is utilized for segmentation associated with churn clients. This paper represents the proposed Hybrid approach is an integration of two techniques named random forest and Support Vector Machine(SVM) that have feature of Artificial bee colony (ABC), provides better and accurate results in the prediction of churn customers.
References
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Index Terms
Computer Science
Information Sciences
Keywords