Research on Company Bankruptcy Prediction Based on Unbalanced Data

Abstract:

This paper discusses the problem of corporate bankruptcy prediction using unbalanced data by innovatively integrating data preprocessing technology and integration algorithm. Firstly， redundant information processing and different sampling methods are used to preprocess unbalanced data. Secondly， a decision tree with Classifier 5.0 （C5.0） and a single hidden layer feedforward neural network are used as the base classifier to select the optimal sampling method by combining with three kinds of resampling data preprocessing technologies. Thirdly， the self-aggregation method is combined to improve the classification performance， and the integration models of the two base classifiers are compared by the area under the receiver operating characteristic curve with 10-fold cross-validation. Finally， the actual data of more than 10 000 Polish manufacturing companies in the database of University of California Irvine are used for experimental verification. The experimental results show that the integrated model combining under-sampling or synthetic minority over-sampling method with neural network archive the best classification performance， which provides positive support for the enterprises to implement bankruptcy prediction.

Key words:binary classification;unbalanced data;neural network;C 5.0 decision tree;integrated methods