Automatic classification method for accounting data results based on decision trees
GE Hongjun
ZHANG Haimin
Abstract：Accounting data has the characteristic of discretization of continuous variables.In the case of concept drift,it is difficult to capture the interaction effects of nonlinear continuous variables in the data,which reduces the accuracy of data classification.To address this,an automatic classification method for accounting data results based on decision trees is proposed.Obtain the statistical probability density feature quantities of the accounting data set,capture the local features of the data,and achieve the discretization processing of continuous variables.The discretization results of continuous variables are fuzzified.A decision tree is constructed by using the maximum gain of fuzzy information to capture the nonlinear relationships and interaction effects among variables.The classification results of multiple decision trees are integrated to reduce the deviation of a single decision tree and achieve the automatic classification of accounting data results.The experimental results show that the proposed method can process accounting data with high precision and accurately depict the nonlinear relationships therein.It meets the real-time requirements and can provide timely and reliable data support for financial decision-making.
Keywords：decision treeaccounting dataautomatic classificationcontinuous variable
Publication Date：2025-12-25
Online Publishing Date：2026-01-27(First online date of this platform, not the publication date of the document)
Pages：6( 19-23,30 )