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Authors: Paulius Danenas and Gintautas Garsva

Affiliation: Vilnius University, Lithuania

ISBN: 978-989-8565-10-5

Keyword(s): Support Vector Machines, Linear SVM, Particle Swarm Optimization, Credit Risk, Evaluation, Bankruptcy, Machine Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; 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: A research on credit risk evaluation modelling using linear Support Vector Machines (SVM) classifiers is proposed in this paper. The classifier selection is automated using Particle Swarm Optimization technique. Sliding window approach is applied for testing classifier performance, together with other techniques such as discriminant analysis based scoring for evaluation of financial instances and correlation-based feature selection. The developed classifier is applied and tested on real bankruptcy data showing promising results.

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Paper citation in several formats:
Danenas, P. and Garsva, G. (2012). PSO-based Linear SVM Classifier Selection for Credit Risk Evaluation Modeling Process.In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-10-5, pages 338-341. DOI: 10.5220/0004006403380341

@conference{iceis12,
author={Paulius Danenas. and Gintautas Garsva.},
title={PSO-based Linear SVM Classifier Selection for Credit Risk Evaluation Modeling Process},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2012},
pages={338-341},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004006403380341},
isbn={978-989-8565-10-5},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - PSO-based Linear SVM Classifier Selection for Credit Risk Evaluation Modeling Process
SN - 978-989-8565-10-5
AU - Danenas, P.
AU - Garsva, G.
PY - 2012
SP - 338
EP - 341
DO - 10.5220/0004006403380341

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