An Integration Method Of Classifiers For Abnormal Phone Detection

Harassing and fraud calls have spread like viruses in people's lives, many researchers have proposed some solutions to abnormal phone detection. However, most of these methods are passive detection, cannot give accurate prediction in time. In this work, we worked with operators to obtain a volume of real telecom user data and extract a series of comprehensive features. We propose an integration method of classifiers for abnormal phone detection by applying the machine learning algorithm on the data with unbalance and `dirty data'. Especially, we use bootstrap sampling method and voting strategy to reduce the false prediction of classier due to noise data. The experimental result shows the effectiveness of our method in contrast with traditional classification algorithm.