Journal
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2012
Volume 9, Issue -, Pages 1324-1333Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2012.04.145
Keywords
Support Vector Machines; Particle Swarm Optimization; Genetic Algorithms; credit risk; evaluation; bankruptcy; analysis
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This paper presents a study on credit risk evaluation modeling using linear Support Vector Machines (SVM) classifiers, combined with evolutionary parameter selection using Genetic Algorithms and Particle Swarm Optimization, and sliding window approach. Discriminant analysis was applied for evaluation of financial instances and dynamic formation of bankruptcy classes. The possibilities of feature selection application were also researched by applying correlation-based feature subset evaluator. The research demonstrates a possibility to develop and apply an intelligent classifier based on original discriminant analysis method evaluation and shows that it might perform bankruptcy identification better than original model.
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