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Bankruptcy Prediction using SVM and Hybrid SVM Survey
by
Jayanthi. J,
Suresh Joseph. K,
Vaishnavi. J
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 34 - Number 7 |
| Year of Publication: 2011 |
| Authors: Jayanthi. J, Suresh Joseph. K, Vaishnavi. J |
10.5120/4114-5928
|
Jayanthi. J, Suresh Joseph. K, Vaishnavi. J . Bankruptcy Prediction using SVM and Hybrid SVM Survey. International Journal of Computer Applications. 34, 7 ( November 2011), 39-45. DOI=10.5120/4114-5928
@article{
10.5120/4114-5928,
author = {
Jayanthi. J,
Suresh Joseph. K,
Vaishnavi. J
},
title = { Bankruptcy Prediction using SVM and Hybrid SVM Survey },
journal = {
International Journal of Computer Applications
},
issue_date = { November 2011 },
volume = { 34 },
number = { 7 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = {
39-45
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume34/number7/4114-5928/
},
doi = { 10.5120/4114-5928 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:20:31.086250+05:30
%A Jayanthi. J
%A Suresh Joseph. K
%A Vaishnavi. J
%T Bankruptcy Prediction using SVM and Hybrid SVM Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 34
%N 7
%P 39-45
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Bankruptcy prediction has been a topic of active research for business and corporate organizations since past decades. It is an effective tool to help financial institutions and relevant people to make the right decision in investments, especially in the current competitive environment. The tool provides auditors and managers a chance to identify the problems early. Thus, relevant people have an opportunity to intervene early into affairs problems to reduce the expected cost of bankruptcy failure. The primary objective is to reduce well in advance as much as possible to the loss of relevant beneficial, such as investors, managers and employees. This paper provides a survey of bankruptcy predictions using a novel machine learning approach: support vector machine (SVM) and hybrid SVM. The underlying motivation for using SVM and hybrid SVM are the ability of this methodology to determine accurate forecast bankruptcy data set when the underlying system processes are typically nonlinear, non-stationary and not defined a – priori. SVM and hybrid SVM have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques.
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Index Terms
Computer Science
Information Sciences
Keywords