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Kim, M.-J. and Han, I. (2003) The Discovery of Experts’ Decision Ruels from Qualitative Bankruptcy Data Using Genetic Algorithms. Expert Systems with Application, 25, 637-646,

has been cited by the following article:

• TITLE: Bankruptcy Prediction Using Machine Learning

  AUTHORS: Nanxi Wang

  KEYWORDS: Support Vector Machine, Autoencoder, Neural Network, Bankruptcy, Machine Learning

  JOURNAL NAME: Journal of Mathematical Finance, Vol.7 No.4, November 17, 2017

  ABSTRACT: With improved machine learning models, studies on bankruptcy prediction show improved accuracy. This paper proposes three relatively newly-developed methods for predicting bankruptcy based on real-life data. The result shows among the methods (support vector machine, neural network with dropout, autoencoder), neural network with added layers with dropout has the highest accuracy. And a comparison with the former methods (logistic regression, genetic algorithm, inductive learning) shows higher accuracy.