|Table of Contents|

Group Lasso-Based Feature Selection for Off-networkAnalysis in Multisource Teledata(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2014年04期
Page:
77-
Research Field:
Publishing date:

Info

Title:
Group Lasso-Based Feature Selection for Off-networkAnalysis in Multisource Teledata
Author(s):
Sun Liangjun1; Fan Jianfeng2; Yang Wanqi2; Shi Yinghuan2; Gao Yang2; Zhou Xinmin3
(1.Zhongbo Information Technology Research Institute Company,Nanjing 210012,China)(2.State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210046,China)(3.Forensic Center of Jiangsu Province Public Security Bureau,Nanjing 210024,
Keywords:
telecom companies; customer churn; multisource data; feature selection; Group Lasso
PACS:
TP181
DOI:
-
Abstract:
With the intensified competition in the industry,customer churn analysis is becoming one of the most significant tasks for the telecom companies,which might lead great financial loss to them.Thus,using the data to predict potential off-network customers and then making business decisions to retain these customers,have drawn lots of attention nowadays.In this paper,we present a Group Lasso-based feature selection method to predict the latent off-network customers by analyzing the corresponding multisource teledata.Specifically,we utilize the cross-validation strategy to choose the optimal sets of feature groups.Extensive experiment results show that the proposed approach has the superior performance(the Precision value is 10% higher than the other methods)on a real telecom dataset derived by a certain city in a prefectural city of Jiangsu.

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Last Update: 2014-12-31