Corporate failure prediction is based on the extraction of key factors and early warning based on factor indicators or
characteristic variables related to corporate failure. The current representative failure prediction empirical models include
Credit Scoring, DA, MDA, and so on. However, the above models have problems such as data deviation and uncertain
factor contributions. It is to explore starting from the machine learning model, establishing a combined model, combining
the minimum variance method, to construct the Logistic-SVM combined model. The study found that the total
classification accuracy of the Logistic-SVM combined model is higher than that of the traditional single model, and the
error rate is also lower.
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