关键词: 企业自律性评价, 集成学习范式, 残差预测神经网络, 显式偏差修正, 互联网企业监管

Abstract:

In order to cope with the current situations of low self-discipline， frequent violation events and difficult government supervision of enterprises in the internet environment， a Two-layer ensemble residual prediction Neural Network （TenrepNN） model was proposed to evaluate the self-discipline of enterprises. And by integrating the ideas of Stacking and Bagging ensemble learning， a new paradigm of integrated learning was designed， namely Adjusting. TenrepNN model has a two-layer structure. In the first layer， three base learners were used to predict the enterprise score preliminarily. In the second layer， the idea of residual correction was adopted， and a residual prediction neural network was proposed to predict the output deviation of each base learner. Finally， the final output was obtained by adding the deviations and the base learner scores together. On the enterprise self-discipline evaluation dataset， compared with the traditional neural network， the proposed model has the Root Mean Square Error （RMSE） reduced by 2.7%， and the classification accuracy in the self-discipline level reached 94.51%. Experimental results show that by integrating different base learners to reduce the variance and using residual prediction neural network to decrease the deviation explicitly， TenrepNN model can accurately evaluate enterprise self-discipline to achieve differentiated dynamic supervision.

Key words: enterprise self-discipline evaluation, ensemble learning paradigm, residual prediction neural network, explicit deviation correction, internet enterprise supervision