山西大学学报（自然科学版）2024，Vol.47Issue(1)：155-165,11.DOI:10.13451/j.sxu.ns.2023099
一种可解释的相对贫困识别与预警模型
An Explainable Model for Identification and Early Warning of Relative Poverty
摘要
Abstract
The necessary conditions for long-term governance of relative poverty are to build a relative poor population identification system and a monitoring and early warning mechanism,as well as to enhance the pertinence and effectiveness of the monitoring and early warning mechanism.Due to the relatively limited theoretical and algorithmic research on relative poverty identification,in this paper,we propose an explainable model for identification and early warning of relative poverty,i.e.IEWRP model.This model takes the data of China Household Tracking Questionnaire(CFPS2018)from 2018 to 2020 as the research object,and uses technique of variance analysis to select features from the original data set.Then,the IEWRP model is constructed by gradient boosting,and com-pared with machine learning algorithms such as CART(Classification and Regression Tree),XGBoost and LightGBM.Finally,an in-terpretability analysis is conducted on the the necessity and importance of relevant features that affect relative poverty recognition with the shapley additive explanation model,identifying the main features that affect relative poverty recognition.Experimental re-sults show that the prediction accuracy,precision,recall rate,F1 value and AUC(Area Under Curve)value of IEWRP model are 89.4%,90.6%,95.3%,92.9%and 0.95,respectively.The accuracy,precision,F1 value and AUC value are increased by 0.15%,0.28%,0.09%and 0.23%,respectively.关键词
Gradient Boosting模型/SHAP模型/相对贫困预测/特征分析Key words
gradient boosting model/shapley additive explanation model/relative poverty prediction/feature analysis分类
信息技术与安全科学引用本文复制引用
史颖,丁天琪,祁晓博,亓慧..一种可解释的相对贫困识别与预警模型[J].山西大学学报（自然科学版）,2024,47(1):155-165,11.基金项目
山西省哲学社会科学规划课题(2021YJ078) （2021YJ078）
国家自然科学基金(62276161) （62276161）
山西省基础研究计划(自由探索)项目(20210302123334) （自由探索）
山西省高等学校科技创新项目(2021L443) （2021L443）
