Detection of Distributed Financial Bad Data of Enterprises Based on Isolated Forest Algorithm
In order to realize the efficient and accurate detection of enterprise distributed financial bad data,and provide enterprises with important data guarantee for financial security decision-making,based on the isolated forest algorithm,the detection of enterprise distributed financial bad data is studied in depth.By analyzing the enterprise distributed financial metadata management system,combining the metadata catalog in the metadata warehouse to map the actual enterprise distributed financial data list,and extracting the actual enterprise distributed financial data;pre-processing the data from the perspectives of noise interference processing,data missing filling,combining the Z-score method with the median interpolation method,to ensure the quality of the enterprise distributed financial data;according to the data variance,standard deviation,skewness,kurtosis and other statistics,calculate the distribution characteristics of bad data in the completed preprocessed data,and based on the isolated forest algorithm,integrate the binary tree structure of the isolated tree to realize the efficient and accurate detection of the bad data of enterprise distributed finance.The experimental results show that:after using the design method to preprocess the data in the data set,it can effectively solve the abnormal spatial distribution state of the data,effectively fill in the missing part of the data,and repair the aberration state generated by the influence of the collection noise interference,which has a good practical application effect.And the highest value of detection consumption time is 5.4s,and the highest value of detection accuracy is 0.93,which has certain advantages in detection efficiency and detection accuracy.
万方数据
维普
