Research on Demographic Prediction Based on Ensemble Classifiers

Abstract:

User attributes play an important role in personalized service. The prediction of the user's property based on mobile phone data has gradually become a new direction. In this paper, we use two independent attributes: average daily usage time and number of application categories. The basic attribute and the concept of the auxiliary attribute are proposed. In this paper, firstly, the auxiliary attributes of all unlabeled samples are discretized by non-supervised method. And then calculate the Hellinger Distance of auxiliary property categories, which is the characteristic weight of the basic attribute. Input the basic attributes and the characteristic weight to the base classifier of the ensemble classifier training model, introducing random forest with out of sample accuracy as the base classifier weights, finally we get the final classification results. The experimental results show that the ensemble classifiers framework can improve the effect of user attribute prediction.