Authors:
Egidijus Merkevičius
1
;
Gintautas Garšva
1
;
Stasys Girdzijauskas
1
and
Vitolis Sekliuckis
2
Affiliations:
1
Kaunas Faculty of Humanities, Vilnius University, Lithuania
;
2
Kaunas University of Technology, Lithuania
Keyword(s):
Bankruptcy, Self-organizing maps, Neural network, prediction, multivariate discriminate model.
Related
Ontology
Subjects/Areas/Topics:
Applications of Expert Systems
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Computational Intelligence
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Industrial Applications of Artificial Intelligence
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The multivariate discriminate models have been used in area of bankruptcy analysis for many years. In this paper we suggest to conjunct the principles of traditional discriminate bankruptcy models with modern methods of machine learning. We propose the forecasting model based on Self-organizing maps, where inputs are indicators of multivariate discriminate model. Accuracy of forecasting is improved via changing weights with supervised learning type ANN. We’ve presented results of testing of this model in various aspects.