Credit rating analysis with support vector machines and artificial bee colony algorithm

Mu Yen Chen; Chia Chen Chen; Jia Yu Liu

doi:10.1007/978-3-642-38577-3_54

Credit rating analysis with support vector machines and artificial bee colony algorithm

Mu Yen Chen, Chia Chen Chen, Jia Yu Liu

Department of Engineering Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Recently, credit rating analysis for financial engineering has attracted many research attentions. In the previous, statistical and artificial intelligent methods for credit rating have been widely investigated. Most of them, they focus on the hybrid models by integrating many artificial intelligent methods have proven outstanding performances. This research proposes a newly hybrid evolution algorithm to integrate artificial bee colony (ABC) with the support vector machine (SVM) to predict the corporate credit rating problems. The experiment dataset are select from 2001 to 2008 of Compustat credit rating database in America. The empirical results show the ABC-SVM model has the highest classification accuracy. Hence, this research presents the ABC-SVM model could be better suited for predicting the credit rating.

Original language	English
Title of host publication	Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings
Pages	528-534
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-642-38577-3_54
Publication status	Published - 2013
Event	26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013 - Amsterdam, Netherlands Duration: 2013 Jun 17 → 2013 Jun 21

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7906 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013
Country	Netherlands
City	Amsterdam
Period	13-06-17 → 13-06-21

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-38577-3_54

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Chen, M. Y., Chen, C. C., & Liu, J. Y. (2013). Credit rating analysis with support vector machines and artificial bee colony algorithm. In Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings (pp. 528-534). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7906 LNAI). https://doi.org/10.1007/978-3-642-38577-3_54

Chen, Mu Yen ; Chen, Chia Chen ; Liu, Jia Yu. / Credit rating analysis with support vector machines and artificial bee colony algorithm. Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings. 2013. pp. 528-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Recently, credit rating analysis for financial engineering has attracted many research attentions. In the previous, statistical and artificial intelligent methods for credit rating have been widely investigated. Most of them, they focus on the hybrid models by integrating many artificial intelligent methods have proven outstanding performances. This research proposes a newly hybrid evolution algorithm to integrate artificial bee colony (ABC) with the support vector machine (SVM) to predict the corporate credit rating problems. The experiment dataset are select from 2001 to 2008 of Compustat credit rating database in America. The empirical results show the ABC-SVM model has the highest classification accuracy. Hence, this research presents the ABC-SVM model could be better suited for predicting the credit rating.",

author = "Chen, {Mu Yen} and Chen, {Chia Chen} and Liu, {Jia Yu}",

note = "Copyright: Copyright 2013 Elsevier B.V., All rights reserved.; 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013 ; Conference date: 17-06-2013 Through 21-06-2013",

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Chen, MY, Chen, CC & Liu, JY 2013, Credit rating analysis with support vector machines and artificial bee colony algorithm. in Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7906 LNAI, pp. 528-534, 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Amsterdam, Netherlands, 13-06-17. https://doi.org/10.1007/978-3-642-38577-3_54

Credit rating analysis with support vector machines and artificial bee colony algorithm. / Chen, Mu Yen; Chen, Chia Chen; Liu, Jia Yu.

Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings. 2013. p. 528-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7906 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Recently, credit rating analysis for financial engineering has attracted many research attentions. In the previous, statistical and artificial intelligent methods for credit rating have been widely investigated. Most of them, they focus on the hybrid models by integrating many artificial intelligent methods have proven outstanding performances. This research proposes a newly hybrid evolution algorithm to integrate artificial bee colony (ABC) with the support vector machine (SVM) to predict the corporate credit rating problems. The experiment dataset are select from 2001 to 2008 of Compustat credit rating database in America. The empirical results show the ABC-SVM model has the highest classification accuracy. Hence, this research presents the ABC-SVM model could be better suited for predicting the credit rating.

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Chen MY, Chen CC, Liu JY. Credit rating analysis with support vector machines and artificial bee colony algorithm. In Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings. 2013. p. 528-534. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-38577-3_54