An abnormal telephone identification model based on ensemble algorithm

Due to the rapid development of the communications industry and the popularization of telephones, more and more personal information leaks and telephone fraud cases have occurred in the life.For the problem of fraudulent calls, there are deficiencies for operators to solve these problems.Inspired by the ensemble algorithm, it was found that the bagging algorithm can solve the classification problem of unbalanced data.This paper proposes an abnormal phone recognition model based on bagging algorithm.In particular, we used PCA dimension reduction in processing data to better mine the effective features of the sample, Multiple training sets are constructed by bootstrap sampling, and the ensemble of multiple training set-trained learners can solve the classification problem of unbalanced abnormal telephone data. Experiments show that the accuracy of prediction results of the abnormal phone recognition model based on the integrated algorithm is better than the prediction results of the single decision tree model, and the problem of unbalanced samples was solved and a relatively ideal prediction effect was achieved.