Research on Financial Anomaly Data Extraction Method based on Random Forest
Due to the poor data stability and many kinds of financial abnormal data extraction meth-ods,it was difficult to extract abnormal data.Therefore,proposes an extraction method based on ran-dom forest.By clarifying the information gain,information storage,search engine and other attrib-utes of financial data,a decision tree was established.The Gini coefficient was used to calculate the category value of each node data in the decision tree,and the same category data was classified into the same level.If the data feature distribution was chaotic,the segmentation method was used to segment all the data in the data set until the most accurate result was obtained through iteration.The selective integration algorithm was introduced into the decision tree.According to the obtained eigenvalues,the data with the same characteristics are divided into the same subcategory to ensure the unity of characteristics.The abnormal data characteristics are input into the decision tree,and the abnormal fi-nancial data values are extracted through feature search.Simulation results show that the proposed method has high extraction accuracy and low false detection rate,and can achieve the desired results with the least number of iterations.
random forest decision treeGini coefficientinformation storage capacitysubcategoryrecall
万方数据
维普
