RETRACTED ARTICLE: Intelligent hybrid model for financial crisis prediction using machine learning techniques
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abellán J, Mantas CJ (2014) Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Syst Appl 41(8):3825–3830
Ala’raj M, Abbod MF (2016) Classifiers consensus system approach for credit scoring. Knowl Based Syst 104:89–105
Atiya AF (2001) Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Trans Neural Netw 12(4):929–935
Chauhan N, Ravi V, Chandra DK (2009) Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks. Expert Syst Appl 36(4):7659–7665
Chen HL et al (2011) An adaptive fuzzy K-nearest neighbor method based on parallel particle swarm optimization for bankruptcy prediction. In: Huang J, Cao L, Srivastava J (eds) Advances in knowledge discovery and data mining. Springer, Berlin, pp 249–264
Evans R, Pfahringer B, Holmes G (2011) Clustering for classification. In: 2011 7th international conference on information technology in Asia (CITA 11). IEEE, pp 1–8
Fedorova E, Gilenko E, Dovzhenko S (2013) Bankruptcy prediction for Russian companies: application of combined classifiers. Expert Syst Appl 40(18):7285–7293
Guojun G, Chaoqu M, Jianhong W (2007) Data clustering: theory, algorithm and application, 1st edn. ASA-SIAM, Philadelphia
Kim MJ, Han I (2003) The discovery of experts’ decision rules from qualitative bankruptcy data using genetic algorithms. Expert Syst Appl 25(4):637–646
Korsunsky AM, Constantinescu A (2006) Work of indentation approach to the analysis of hardness and modulus of thin coatings. Mater Sci Eng A 423(1–2):28–35
Martin VA, Balaji S, Lakshmi TM, Venkatesan VP (2012) An analysis on qualitative bankruptcy prediction using fuzzy ID3 and ant colony optimization algorithm. In: International conference on pattern recognition, informatics and medical engineering, pp 416–421
Martin A, Uthayakumar J, Nadarajan M (2014) Qualitative_Bankruptcy data set. UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/qualitative_bankruptcy. Accessed 1 Oct 2018
Min JH, Lee Y-C (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst Appl 28(4):603–614
Naveen N et al (2010) Differential evolution trained radial basis function network: application to bankruptcy prediction in banks. Int J BioInspir Comput 2(3–4):222–232
Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18:109–131
Paramjeet, Ravi V (2011) Bacterial foraging trained wavelet neural networks: application to bankruptcy prediction in banks. Int J Data Anal Tech Strateg 3(3):261–280
Pietruszkiewicz W (2008) Dynamical systems and nonlinear Kalman filtering applied in classification. In: Proceedings of the 7th IEEE international conference on cybernetic intelligent systems, CIS 2008
Ravi V, Pramodh C (2008) Threshold accepting trained principal component neural network and feature subset selection: application to bankruptcy prediction in banks. Appl Soft Comput 8:1539–1548
Ravisankar P, Ravi V (2009) Failure prediction of banks using threshold accepting trained kernel principal component neural network. In: Proceedings of the IEEE world congress on nature & biologically inspired computing, NaBIC 2009
Reddy KN, Ravi V (2013) Differential evolution trained kernel principal component WNN and kernel binary quantile regression: application to banking. Knowl Based Syst 39:45–56
Sarkar S, Sriram RS (2001) Bayesian models for early warning of bank failures. Manag Sci 47(11):1457–1475
Sharma N, Arun N, Ravi V (2013) An ant colony optimisation and Nelder–Mead simplex hybrid algorithm for training neural networks: an application to bankruptcy prediction in banks. Int J Inf Decis Sci 5(2):188–203
Shin K-S, Lee TS, Kim H-J (2005) An application of support vector machines in bankruptcy prediction model. Expert Syst Appl 28(1):127–135
Sun L, Shenoy PP (2007) Using Bayesian networks for bankruptcy prediction: some methodological issues. Eur J Oper Res 180(2):738–753
Tkaczyk ER, Mauring K, Tkaczyk AH et al (2008) Control of the blue fluorescent protein with advanced evolutionary pulse shaping. Biochem Biophys Res Commun 376(4):733–737
Tomczak S (2016) Polish companies bankruptcy data data set. UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data. Accessed 15 Sept 2018
Tsai C-F, Wu J-W (2008) Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Syst Appl 34(4):2639–2649
ul Hassan E, Zainuddin Z, Nordin S (2017) A review of financial distress prediction models: logistic regression and multivariate discriminant analysis. Indian Pac J Account Finance 1(3):13–23
Vasu M, Ravi V (2011) Bankruptcy prediction in banks by principal component analysis threshold accepting trained wavelet neural network hybrid. In: Proceedings of the international conference on data mining, USA
Vieira SM, Sousa JMC, Runkler TA (2010) Two cooperative ant colonies for feature selection using fuzzy models. Expert Syst Appl 37(4):2714–2723
Wang Y, Li B, Weise T (2010) Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems. Inf Sci 180(12):2405–2420
Zhang G et al (1999) Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. Eur J Oper Res 116(1):16–32
Zhang Y, Wu L, Wang S (2011) UCAV path planning based on FSCABC. Information 14(3):687–692
Zhang Y, Wu L, Wang S (2013a) UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization. Math Probl Eng 2013:9