The week's pick
Random Articles
Reseach Article
An Improved Binary Classification Framework for Investment Class Rating
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 119 - Number 21 |
| Year of Publication: 2015 |
| Authors: Ajit Kumar Das, Sudarsan Padhy |
10.5120/21363-4384
|
Ajit Kumar Das, Sudarsan Padhy . An Improved Binary Classification Framework for Investment Class Rating. International Journal of Computer Applications. 119, 21 ( June 2015), 33-40. DOI=10.5120/21363-4384
Abstract
This paper presents a binary classification scheme for investment class rating using support vector machine (SVM). The suggested SVM model is trained offline and takes twelve financial ratios as attributes from different standard investment companies as inputs and correctly classify whether it is a good investment grade or bad investment grade company as output. The overall performance of SVM strongly depends on the regularization parameter C and kernel parameter ?. Hence, we propose the PSO based optimization technique using mean square error (MSE) as the fitness function to optimize the value of C and ?. The proposed scheme is implemented using Matlab and Libsvm tool. Comparison is made in terms of different performance measures like classification accuracy, sensitivity, specificity, precision, confusion matrix etc. From experimental results and analysis, it is observed that the proposed scheme has a superior performance as compared to SVM based approach without parameter optimization and neural network based scheme.
References
- Jackson JD, Boyd JW (1988) A statistical approach to modelling the behaviour of bond raters. J Behav Econ 17:173–193
- Kamstra M, Kennedy P, Suan TK (2001) Combining bond rating forecasts using logic and Finance Rev 37:75–96
- Kaplan RS, Urwitz G (1979) Statistical models of bond ratings: a methodological inquiry. J Bus 52:231–261
- Brennan D, Brabazon A (2004) corporate bond rating using neural networks. In: Proc of the conf on artificial intelligence, Las Vegas, pp 161–167
- Huang Z, Chen H (2004) Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support System 37:543–558
- Ammar S, Duncombe W, Hou Y (2001) Using fuzzy rule-based systems to evaluate overall financial performance of governments: An enhancement to the bond rating process. Public Budget Finance 21:91–110
- Brabazon A, O'Neill M (2006) Credit classification using grammatical evolution. Inform 30:325–335
- Delahunty A, OCallaghan D (2004) artificial immune systems for the prediction of corporate failure and classification of corporate bond ratings. University College Dublin, Dublin
- Kim KS, Han I (2001) The cluster-indexing method for case based reasoning using self-organizing maps and learning vector quantization for bond rating cases. Exp System Application 21:147–156
- Garavaglia S (1991) An application of a counter-propagation neural networks: Simulating the Standard & Poor's corporate bond rating system. In: Proc. of the 1st int conf on artificial intelligence on Wall Street, pp 278–287
- Lee YCh (2007) Application of support vector machines to corporate credit rating prediction. Exp Syst Appl 33:67–74
- Abe S (2005) Support vector machines for pattern classification, Springer-Verlag, London
- Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
- Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, New Jersey
- Jan Henning Trustorff, Paul Markus Konrad, Jens Leker (2010), Credit risk prediction using support vector machines. Rev Quant Finance Acc 36:565-581
- Peter Hajek, Vladimir Olej (2011), Credit rating modeling by kernel-based approaches with supervised and semi-supervised learning. Neural Computing & Application 20:761-773
- Tony Van Gestel , Bart Baesens , Dr. Ir , Joao Garcia , Peter Van Dijcke (2003), A support vector machine approach to credit scoring, journal of machine learning, Spinger, 4(3)
- Shom Prasad Das, Sudarsan Padhy (2012), Support vector machines for prediction of futures prices in Indian stock market, International journal of computer application 41(3)
- Vapnik, V. N. , "The Nature of Statistical Learning Theory", Springer, 2nd edition, 1999
- Shawe-Taylor, J. , and Cristianini, N. "Kernel Methods for Pattern Analysis" , Cambridge UP, 2004.
- Hung Wua, Gwo-Hshiung Tzeng, and Rong-Ho Lin. A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Systems with Applications, 36:4725-4735, 2009.
- A GA based feature selection and parameters optimization for SVM, Expert system with applications, 31:231-240, 2006.
- J. Kennedy and R. C. Eberhart. Particle Swarm Optimisation. In Proc. IEEE International conf. on neural networks, volume IV, pages 265 – 270, November
- Kennedy and R. C. Eberhart. A discrete binary version of the particle swarm algorithm. In IEEE International Conference on Computational Cybernetics and simulation volume 5, pages 4104 –4108, October 1997
- Huiyuan Fan. A modification to particle swarm optimisation algorithm Engineering Computations, 19(8):970–989, 2002
Index Terms
Keywords