企业所得税税收风险识别中随机森林算法的应用研究

Research on the Application of Stochastic Forest Algorithm in the Risk Identification of Enterprise Income Tax

税收是国家重要的财政收入来源且具有强制性,某些纳税人会以各种手段避免税款或者不足额缴纳税款,这样极易造成税收风险。以随机森林算法和税收风险理论为基础,采用约简决策树的方式进一步提高了随机森林算法的分类性能,构建了对房地产行业的企业所得税税收风险识别模型。模型在性能检测实验中获得了90.20%的正确率和88.70%的F1分数,且运行时间较原始的随机森林算法减少了33.33%。实验结果表明:对随机森林算法作出的改进是正确、有效的,且证实了构建的税收风险识别模型用于企业所得税税收风险的可行性和优越性,一方面为税务机关进行企业风险管理提供了借鉴;另一方面也为规范企业涉税风险管理提供了支撑。

Tax is an important source of fiscal revenue for the country and is mandatory,which leads some taxpayers to avoid high taxes or not pay taxes in full by various means,which is extremely easy to cause tax risks.Based on the random forest algorithm and the theory of tax risk,the research further improves the classification performance of the random forest algorithm by reducing the decision tree,and then constructs the enterprise income tax risk identification model for the real estate industry.The model achieves 90.20%accuracy and 88.70%F1 score in the performance test experiment,and the running time is 33.33%less than the original random forest algorithm.The experimental results show that the improvement of the random forest algorithm is correct and effective,and confirm the feasibility and superiority of the tax risk identification model used for the enterprise income tax risk.On the one hand,it provides a reference for the tax authorities to carry out enterprise risk management,on the other hand,it also provides a support for standardizing the enterprise tax-related risk management.