Mô hình phân biệt khách hàng vay bị vỡ nợ sử dụng máy vector hỗ trợ nhạy cảm với chi phí được cải tiến bởi PSO

Bhekisipho T (2009) Multiple classifier application to credit risk assessment. Expert Syst Appl 37(4):3326–3336 Ma RW, Tang CY (2007) Building up default predicting model based on logistic model and misclassification loss. Syst Eng Theory Pract 27:33–38 Ke KL, Feng ZX (2008) Short-term loan default prediction based on integration of rough sets and genetic algorithm. Syst Eng Theory Pract 28(4):27–34 Min SH, Lee J, Han I (2006) Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Syst Appl 31(3):652–660 Min JH, Jeong C (2009) A binary classification method for bankruptcy prediction. Expert Syst Appl 36:5256–5263 Yang ZJ, You WJ, Ji GL (2011) Using partial least squares and support vector machines for bankruptcy prediction. Expert Syst Appl 38:8336–8342 Huang Z, Chen H, Hsu CJ et al (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis Support Syst 37(4):543–558 Ong CS, Huang JJ, Tzeng GH (2005) Building credit scoring models using genetic programming. Expert Syst Appl 29(1):41–47 Chawla N, Japkowicz N, Kolcz A (2004) Editorial: special issues on learning from imbalanced data sets. SIGKDD Explor 6:1–6 Mccarthy K, Zabar B, Weiss G (2005) Does cost-sensitive learning beat sampling for classifying rare classes? In: proceedings of the ACM SIGKDD first international workshop on utility-based data mining. ACM Press, pp. 69–75 Tsai CH, Chang LC, Chiang HC (2009) Forecasting of ozone episode days by cost-sensitive neural network methods. Sci Total Environ 407(6):2124–2135 Maloof M (2003) Learning when data sets are imbalanced and when costs are unequal and unknown. In: proceedings of the ICML-2003 workshop: learning with imbalanced data sets II, pp. 73–f80 Zhou ZH, Liu XY (2006) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 18:63–77 Li Z, Ling L, Lian D (2012) Business intelligence in enterprise computing environment. Inf Technol Manage 13:297–310 Yu L, Yao X, Wang SY, Lai KK (2011) Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection. Expert Syst Appl 38:15392–15399 Pontil M, Verri A (1998) Support vector machines for 3D object recognition. IEEE Trans Pattern Anal Mach Intell 20(6):637–646 Yu GX, Ostrouchov G, Geist A, et al (2003) An SVM based algorithm for identification of photosynthesis-specific genome features. Second IEEE computer society bioinformatics conference. CA, USA, pp. 235–243 Joachims T (1998) Text categorization with SVM: learning with many relevant features. In: proceedings of ECML-98,10th European conference on machine learning, Vol. 1398 Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: proceedings of ACM international conference on multimedia, pp. 107–118 Wu G, Chang E (2003) Class-boundary alignment for imbalanced dataset learning. In: ICML 2003 workshop on learning from imbalanced data sets II. Washington, DC Cristianini N, Kandola J, Elisseeff A, et al (2001) On kernel target alignment. In: Advances in neural information processing systems, vol 14, pp 367–373 Veropoulos K, Campbell C, Cristianini N (1999) Controlling the sensitivity of support vector machines. In: Dean T (ed) IJCAI: proceedings of international joint conference on artificial intelligence. Morgan Kaufmann, Stockholm, pp 55–60 Pardo M, Sberveglieri G (2005) Classification of electronic nose data with support vector machines. Sens Actuators B Chem 107(2):730–737 Pai PF, Hong WC (2005) Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers Manage 46(17):2669–2688 Huang CL, Chen MC, Wang CJ (2007) Credit scoring with A data mining approach based on support vector machines. Expert Syst Appl 33(4):847–856 Lin SW, Ying KC, Chen SC et al (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35(4):1817–1824 Zhang L, Jack LB, Nandi AK (2005) Fault detection using genetic programming. Mech Syst Signal Process 19:271–289 Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240 Huang CL, Dun JF (2008) A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Appl Soft Comput 8:1381–1391 Cao J, Lu HK, Wang WW et al (2012) A novel five-category loan-risk evaluation model using multiclass LS-SVM By PSO. Int Journal Inf Technol Decis Mak 11(4):857–874 Burgers CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167 Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: proceeding of the IEEE congress on evolutionary computation, pp. 69–73 Xia KW, Dong Y, Du HL (2007) Oil layer recognition model of LS-SVM based on improved PSO algorithm. Control Decis 22(12):1385–1389 Tao Z, Xu BD, Wang DW et al (2003) Rough set knowledge reduction approach based on GA. Syst Eng 21(4):116–122 Ke KL, Feng ZX (2008) Five-category classification of loan risk based on integration of rough sets and neural network system. Control Theory Appl 25(4):759–763 Kohonen T (1989) Self-organization and associative memory. Springer-Verlag, New York Kohonen T (1995) Self-organizing maps. Springer, Berlin. Vol.27, No. 2, pp. 278–279 Wu DS, Liang L (2004) Research of credit score based on V-fold cross-validation and elman neural networks. Syst Eng Theory Pract 4:92–98 Xue F, Ke KL (2008) Five-category evaluation of commercial bank’s loan based on integration of rough sets and neural network. Syst Eng Theory Pract 1:40–45 Zhang M, Zhou ZF (2009) An evaluation model for credit risk of enterprise based on multi-objective programming and support vector machines. China soft sci mag 04:185–190 Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Software available at: http://www.csie.ntu.edu.tw/cjlin/libsvm Oos P, Vanhoof K, Ooghe H (1999) Credit classification: a comparison of logic models and decision. In: proceedings of European conference on machine learning. Chemnitz: [s. n.]