Research on prediction method of poverty-returning risk for poverty alleviation population base on stacking ensemble learning
The poverty-returning risk of the poverty alleviation population is a major factor on the results of poverty eradication and rural revitalization.Accurate prediction of the potential poverty-returning risk of the poverty alleviation population plays a crucial role in guiding the implementation of policies,allocation of resources,and risk assessment.This paper proposed a prediction method based on Stacking ensemble learning for the poverty-returning risk of the poverty alleviation population.taking The monitoring data after desensitization of the poverty alleviation households in Province H was analyzed to identify and filter the key features that significantly affect the poverty-returning risk after correlation analysis and importance ranking of the data features,whose key features were adopted in inter-model correlation analysis of the independent models such as Random Forest(RF),Naive Bayes(NB),Support Vector Machine(SVM),etc.The Stacking ensemble learning prediction model was conducted with RF meta-learner using eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost)and SVM that have lower correlation and higher prediction accuracy.The model was trained and validated by dividing 412 919 data into training and validation sets in 7:3,and the model effect was evaluated using accuracy,precision,recall and F1-Score.The experimental results showed that all evaluation indexes of the poverty-returning risk prediction model based on Stacking ensemble learning were better than that of a single model,and its prediction accuracy was improved by 3.64%,10.96%,3.15%,2.29%,and 5.41%compared with RF,NB,SVM,XGBoost,and AdaBoost,respectively,and finally reached 95.65%,which verified the effectiveness of the method proposed in this paper.The study provided new solution ideas for consolidating and expanding the results of poverty eradication and improving the timeliness of returning to poverty dynamic monitoring and warning.
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