A genetic algorithm-based approach to cost-sensitive bankruptcy prediction

Expert Systems with Applications

Volumn 38, Issue 10, 2011, Pages 12939-12945

  Chen, Ning ^a   Ribeiro, Bernardete ^b   Vieira, Armando S ^a   Duarte, João ^a   Neves, João C ^c  

^a INSTITUTO SUPERIOR DE ENGENHARIA DO PORTO   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

^c UNIVERSITY OF LISBON   (Portugal)

Author keywords

Classification; Cost sensitive learning; Feature selection; Genetic algorithm; Learning vector quantization; Neural network

Indexed keywords

BANKRUPTCY PREDICTION; CLASSIFICATION; COST-SENSITIVE; COST-SENSITIVE LEARNING; EVOLUTIONARY APPROACH; EXPERIMENTAL EVIDENCE; FEATURE SELECTION; LEARNING VECTOR QUANTIZATION; LEARNING VECTOR QUANTIZATION NETWORKS; MISCLASSIFICATIONS; PREDICTION CAPABILITY; PREDICTION MODEL; PREDICTION OF BANKRUPTCY; PREDICTION SYSTEMS; REAL WORLD DATA;

FORECASTING; GENETIC ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTIMIZATION; VECTOR QUANTIZATION; VIRTUAL REALITY;

COSTS;

EID: 79957982142     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.04.090     Document Type: Article

References (20)

1. M.A. Boyacioglu, Y. Kara, and O.K. Baykan Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey Expert Systems with Applications 36 2009 3355 3366
2. M. Castellani, and N.C. Marques FeaSANNT - An embedded evolutionary feature selection approach for neural network classifiers VIMation Journal Knowledge, Service & Production: IT as an Enabler 1 2008 46 53
3. S. Cho, H. Hong, and B.C. Ha A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the mahalanobis distance: For bankruptcy prediction Expert Systems with Applications 37 2010 3482 3488
4. D.B. Fogel Evolutionary computation: Toward a new philosophy of machine intelligence 2nd ed. 2000 IEEE Press New York
5. M.J. Kima, and D.K. Kang Ensemble with neural networks for bankruptcy prediction Expert Systems with Applications 37 2010 3373 3379
6. H.C. Koh The sensitivity of optimal cutoff points to misclassification costs of type I and type II errors in the going-concern prediction context Journal of Business Finance and Accounting 17 1992 187 197
7. T. Kohonen Self-organizing maps 3rd ed. 2001 Springer-Verlag
8. T. Lee, and I. Chen A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines Expert Systems with Applications 28 4 2005 743 752 (Pubitemid 40583852)
9. S.W. Lin, K.C. Ying, S.C. Chen, and Z.J. Lee Particle swarm optimization for parameter determination and feature selection of support vector machine Expert Systems with Applications 35 2008 1817 1824
10. Y. Marinakis, M. Marinaki, M. Doumpos, and C. Zopounidis Ant colony and particle swarm optimization for financial classification problems Expert Systems with Applications 36 2009 10604 10611
11. S.H. Min, J. Lee, and I. Han Hybrid genetic algorithms and support vector machines for bankruptcy prediction Expert Systems with Applications 31 2006 652 660 (Pubitemid 43841989)
12. S. Nanda, and P. Pendharkar Linear models for minimizing misclassification costs in bankruptcy prediction International Journal of Intelligent Systems in Accounting, Finance & Management 10 2001 155 168
13. J.C. Neves, and A. Vieira Improving bankruptcy prediction with hidden layer learning vector quantization European Accounting Review 15 2 2006 253 271
14. P.C. Pendharkar A threshold varying bisection method for cost sensitive learning in neural networks Expert Systems with Applications 34 2008 1456 1464 (Pubitemid 350117512)
15. P. Pendharkar, and S. Nanda A misclassification cost-minimizing evolutionary - Neural classification approach Naval Research Logistics 53 5 2006 432 447 (Pubitemid 44117539)
16. K.P. Ravi, and V. Ravi Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review European Journal of Operational Research 180 1 2007 1 28 (Pubitemid 46123864)
17. Laboratory of Computer and Information Sciences & Neural Networks Research Center, Helsinki University of Technology: SOM Toolbox 2.0. Available from: .
18. Y.M. Sun, M.S. Kamela, A.K.C. Wong, and Y. Wang Cost-sensitive boosting for classification of imbalanced data Pattern Recognition 40 2007 3358 3378 (Pubitemid 47223287)
19. N.H. Vo, and Y. Won Classification of unbalanced medical data with weighted regularized least squares Frontiers in the Convergence of Bioscience and Information Technologies 2007 347 352
20. D. West Neural network credit scoring models Computers and Operations Research 27 2000 1131 1152 (Pubitemid 30398142)