Abnormal Telephone Identification via an Ensemble-based Classification Framework

摘要：

　　Abnormal telephone that often appeared in our daily life has nearly crazed all people.Most of existing solutions to the problem can-not identify them in time with low efficiency and poor prediction.By cooperating with a telecom company,we have collected a cer-tain amount of telecom user data.But,by analysis on the data,we found that it is a special classification problem in the case of small-scale data with noise samples.In this paper,we propose an ensemble-based classification framework,which first generates multiple training sets by partly resampling on the original data,then build classifiers for every of the generated training sets and finally combines the classifiers in the ensemble way.We conduct experiments on real-life data to evaluate the performance of our framework in comparison with some practical classification algo-rithms.The empirical result and analysis demonstrate that our framework can achieve a significant increase in accuracy for such difficult prediction task,especially when having noise samples.

关键词： Abnormal telephone Telecom security Classification Ensemble Learning

作者: Ke Ji Yahan Yuan Ruanyuan Sun Lin Wang Kun Ma Zhenxiang Chen

作者单位: School of Information Science and Engineering University of Jinan Jinan 250022,China

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 613-618

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）