Share Email Print
cover

Proceedings Paper

Learning to rank-based abnormal phone analysis in environment of telecommunication big data
Author(s): Jian Liu; Ke Ji II; Runyuan Sun III; Kun Ma IV
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

It is very important to find out abnormal calls and take effective control measures, but most of the current solutions are passive processing technology and lack active detection methods. Based on the existing telecom big data, through the statistical analysis of abnormal telephone behavior, the salient features which could represent the abnormal telephone were found. Then the the active detection method of abnormal telephone was designed based on the Ranking SVM sorting learning machine learning method. The experimental results on real datasets show that the proposed method can achieve higher accuracy under different sample sizes.

Paper Details

Date Published: 26 July 2018
PDF: 9 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280M (26 July 2018); doi: 10.1117/12.2501789
Show Author Affiliations
Jian Liu, Univ. of Jinan (China)
Ke Ji II, Univ. of Jinan (China)
Runyuan Sun III, Univ. of Jinan (China)
Kun Ma IV, Univ. of Jinan (China)


Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

© SPIE. Terms of Use
Back to Top