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A Study of Data Mining Techniques in Bankruptcy Prediction  

Lee, Kidong (Dept.of Business Administration, University of Incheon)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.28, no.2, 2003 , pp. 105-127 More about this Journal
Abstract
In this paper, four different data mining techniques, two neural networks and two statistical modeling techniques, are compared in terms of prediction accuracy in the context of bankruptcy prediction. In business setting, how to accurately detect the condition of a firm has been an important event in the literature. In neural networks, Backpropagation (BP) network and the Kohonen self-organizing feature map, are selected and compared each other while in statistical modeling techniques, discriminant analysis and logistic regression are also performed to provide performance benchmarks for the neural network experiment. The findings suggest that the BP network is a better choice among the data mining tools compared. This paper also identified some distinctive characteristics of Kohonen self-organizing feature map.
Keywords
Data Mining; Kohonen Self-Organizing Feature Map; BP; Classification;
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