Abstract: With the development of Internet, shopping online has become an increasing choice for people. In order to better achieve the purpose of helping customers to recommend products, the feature of original data is extracted and the feature of the data is selected by mutual information method. The improved EasyEnsemble algorithm is used to deal with the problem of category imbalance, and the defect of under-sampling is compensated by integration strategy. The sample data is fully utilized and the influence caused by positive and negative sample difference is reduced. Finally, the softvoting method is used to combine XGBoost and random forest into a final classifier for prediction, which is compared with the single algorithm, so as to get better results. Based on the data provided by Alibaba Tianchi Competition, the precision rate P, recall R and F1 values are taken as evaluation indexes to compare with the current popular machine learning algorithms respectively to verify the effectiveness of this method.

Key words: mutual information, class-imbalance, EasyEnsemble, XGBoost