Abstract
The self-organizing map is used for analysis of financial statements, focusing on bankruptcy prediction. The phenomenon of going bankrupt is analyzed qualitatively, and companies are also classified into healthy and bankrupt-prone ones. In the qualitative analysis, the self-organizing map is used in a supervised manner: both input and output vectors are represented in the weight vector of each unit, and during training, the whole weight vector is updated, but the best-matching unit search is based on the input vector part only. In the quantitative analysis, three classifiers that utilize the self-organizing map are compared to linear discriminant analysis and learning vector quantization. A modification of the learning vector quantization algorithm to accommodate the Neyman–Pearson classification criterion is also presented.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
