| Mobile-Customer Identity Recognition |
| |
| 作者姓名： | LI Zhan XU Ji-sheng XU Min SUN Hong |
| |
| 作者单位： | [1]School of Electronic Information, Wuhan University, Wuhan 430072, Hubei, China [2]Department of Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China |
| |
| 基金项目： | Supported by Guangdong Mobile Communication Company Limited Key Item （19984001） |
| |
| 摘 要： | By utilizing artificial intelligence and pattern rec ognition techniques, we propose an integrated mobile-customer identity recognition approach in this paper, based on cus tomer＇s behavior characteristics extracted from the customer information database. To verify the effectiveness of this approach, a test has been run on the dataset consisting of 1 000 customers in 3 consecutive months.The result is compared with the real dataset in the fourth month consisting of 162 customers, which has been set as the customers for recognition. The high correct rate of the test （96.30%）, together with 1. 87% of the judge-by-mistake rate and 7.82% of the leaving-out rate, demonstrates the effectiveness of this approach.
|
| 关 键 词： | 移动身份鉴别 遗传算法 模糊集 人工智能技术 |
| 文章编号： | 1007-1202（2005）06-1013-06 |
| 收稿时间： | 2005-01-04 |
Mobile-customer identity recognition |
| |
| LI Zhan XU Ji-sheng XU Min SUN Hong.Mobile-Customer Identity Recognition[J].Wuhan University Journal of Natural Sciences,2005,10(6):1013-1018. |
| |
| Authors: | Li Zhan Xu Ji-sheng Xu Min Sun Hong |
| |
| Institution: | (1) School of Electronic Information, Wuhan University, 130072 Wuhan, Hubei, China;(2) Department of Electronic Information, Huazhong University of Science and Technology, 430074 Wuhan, Hubei, China |
| |
| Abstract: | By utilizing artificial intelligence and pattern recognition techniques, we propose an integrated mobile-customer identity
recognition approach in this paper, based on customer’s behavior characteristics extracted from the customer information database.
To verify the effectiveness of this approach, a test has been run on the dataset consisting of 1000 customers in 3 consecutive
months. The result is compared with the real dataset in the fourth month consisting of 162 customers, which has been set as
the customers for recognition. The high correct rate of the test (96.30%), together with 1.87% of the judge-by-mistake rate
and 7.82% of the leaving-out rate, demonstrates the effectiveness of this approach.
Foundation item: Supported by Guangdong Mobile Communication Company Limited Key Item (19984001)
Biography: LI Zhan (1978-), male, Ph. D candidate, research direction: data mining and data processing. |
| |
| Keywords: | customer identity recognition genetic algorithms fuzzy set |
| 本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录！ |
|
