关键词: 返贫预测, 过采样技术, 集成学习, 融合模型

Abstract: While China has made the decisive achievement on working on poverty alleviation, there are still some people out of poverty who exist risk of returning to poverty. Based on the unbalanced data set, this paper used the model of SMOTE to do sampling process for multi-class samples of returning to poverty. The sample’s ratio of returned to poverty and non-returned to poverty is 3〖DK〗∶1. After that, based on ensemble learning of Stacking, this paper constructed a prediction model of poverty-returning, used grid search to optimize hyper parameters of every model and improved the generalization ability by combining the 10-fold cross-validation. In this paper, four different integration models are used to predict whether the poor households will return to poverty. Compared with the single model, the experiments indicate that the classification results with fusion model are better. Among them, the optimal Acc and F1-score of fusion model are 0.962 and 0.946.

Key words: prediction of poverty-returning, SMOTE, ensemble learning, fusion model