Abnormal telephone recognition method and system based on feature selection and integrated learning

Abstract

The invention discloses an abnormal telephone recognition method and system based on feature selection and integrated learning. The method comprises the steps of constructing a mixed data set; miningsample characteristics through historical call behaviors of the user in a window from the starting time to the ending time; combining and optimizing the characteristics based on the user call behavior, and mining the characteristics with behavior information from the aspects of time, frequency, short message, flow, position and contact person; performing oversampling based on user call behavior samples, increasing the number of few samples, and reducing the influence of sample imbalance on the model; performing feature dimension reduction processing on the user call behavior sample; and establishing a model by using the integrated learning training data set, and carrying out abnormal telephone identification. According to the method, the original information of the sample is fully restoredin a feature mining combination and dimensionality reduction mixing mode, so that the prediction precision is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning and data mining, and in particular relates to a method and system for identifying abnormal calls based on feature selection and integrated learning. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Traditional identification models such as black and white list technology, abnormal traffic detection technology, etc. are currently the main forms of abnormal phone identification. With the rapid development of communication technology and the improvement of people's living standards, fraudulent calls have become more and more low-cost and diverse. Due to the defects of various aspects, the traditional abnormal phone identification model has great defects in the initiative and accuracy of prevention. In order to solve this problem, many schemes have been pr...

AI Technical Summary

Problems solved by technology

[0004] The inventors found that a single sample feature has very limited ability to describe the original sample, and excessively high-dimensional features will increase the complexity of the model and reduce the recognition efficiency of the model, so it is very important to select effective features and contain rich information

[0005] For the first question of sample characteristics: how to obtain sufficient sample characteristics? Many abnormal phone models only use two or three features to judge the sample category, such a model is not advisable

[0011] (1) Insufficient mining of user call behavior features, lack of effective sample information;

[0012] (2) The sample dimension is inappropriate, too high or too low will affect the prediction results;

[0013] (3) In the actual phone samples, the samples of abnormal calls are far smaller than the normal phone samples, so there is a huge problem of sample imbalance, which affects the model results

[0014] (4) The performance of the abnormal phone identification model with a single classification algorithm is poor

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