Design and Study of Zombie Enterprise Classification and Recognition Systems Based on Ensemble Learning

Abstract

The existence of a large number of zombie enterprises will affect the economic development and hinder the transformation and upgrading of economic industries. To improve the accuracy of zombie enterprise identification, this paper takes multidimensional enterprise data as the original data set, divides it into training set and validation set, and gives the corresponding data pre-processing methods. Combined with 14 standardized features, an integrated learning model for zombie enterprise classification and recognition is constructed and studied based on three pattern recognition algorithms. By using the idea of integration and the cross-validation method to determine the optimal parameters, the Gradient Boosting Decision Tree (GBDT), linear kernel Support Vector Machine (SVM) and Deep Neural Network (DNN) algorithms with classification accuracies of 95%, 96% and 96%, respectively, are used as sub-models, and a more comprehensive strong supervision model with a classification accuracy of 98% is obtained by the stacking method in combination with the advantages of multiple sub-models to analyze the fundamental information of 30885 enterprises. The study improves the accuracy of zombie enterprise identification to 98%, builds enterprise portraits based on this, and finally visualizes the classification results through the platform, which provides an auxiliary means for zombie enterprise classification and identification.