A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data

Yu, 2016, A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment, Flexible Serv. Manuf. J., 28, 576, 10.1007/s10696-015-9226-2 Yu, 2008, Credit risk assessment with a multistage neural network ensemble learning approach, Expert Syst. Appl., 34, 1434, 10.1016/j.eswa.2007.01.009 Qian, 2014, A resampling ensemble algorithm for classification of imbalance problems, Neurocomputing, 143, 57, 10.1016/j.neucom.2014.06.021 Fisher, 1936, The use of multiple measurements in taxonomic problems, Ann. Hum. Genet., 7, 179 Mangasarian, 1965, Linear and nonlinear separation of patterns by linear programming, Oper. Res., 13, 444, 10.1287/opre.13.3.444 Wiginton, 1980, A note on the comparison of logit and discriminant models of consumer credit behavior, J. Financial Quant. Anal., 15, 757, 10.2307/2330408 Grablowsky, 1981, Probit and discriminant functions for classifying credit applicants-a comparison, J. Econ. Bus., 33, 254 Baras, 1985, K competing queues with geometric service requirements and linear costs: the (c-rule is always optimal, Syst. Control Lett., 6, 173, 10.1016/0167-6911(85)90037-4 Glover, 1990, Improved linear programming models for discriminant analysis, Decis. Sci., 21, 771, 10.1111/j.1540-5915.1990.tb01249.x Henley, 1996, A k-nearest-neighbour classifier for assessing consumer credit risk, Statistician, 77, 10.2307/2348414 Lai, 2006, Credit risk analysis using a reliability-based neural network ensemble model, 682 Khashman, 2011, Credit risk evaluation using neural networks: emotional versus conventional models, Appl. Soft Comput., 11, 5477, 10.1016/j.asoc.2011.05.011 Lai, 2006, Credit risk evaluation with least square support vector machine, Rough Sets Knowl. Technol., 490, 10.1007/11795131_71 Chen, 2003, Credit scoring and rejected instances reassigning through evolutionary computation techniques, Expert Syst. Appl., 24, 433, 10.1016/S0957-4174(02)00191-4 Huang, 2004, Credit rating analysis with support vector machines and neural networks: a market comparative study, Decis. Support Syst., 37, 543, 10.1016/S0167-9236(03)00086-1 Zhou, 2012, Credit risk evaluation with extreme learning machine, 1064 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Mazurowski, 2008, Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance, Neural Netw., 21, 427, 10.1016/j.neunet.2007.12.031 Tang, 2009, SVMs modeling for highly imbalanced classification, IEEE Trans. Syst. Man Cybernet. Part B (Cybernet.), 39, 281, 10.1109/TSMCB.2008.2002909 Zięba, 2014, Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients, Appl. Soft Comput., 14, 99, 10.1016/j.asoc.2013.07.016 García, 2009, Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems, Appl. Soft Comput., 9, 1304, 10.1016/j.asoc.2009.04.004 Batista, 2004, A study of the behavior of several methods for balancing machine learning training data, ACM SIGKDD Explor. Newslett., 6, 20, 10.1145/1007730.1007735 García, 2012, On the effectiveness of preprocessing methods when dealing with different levels of class imbalance, Knowl.-Based Syst., 25, 13, 10.1016/j.knosys.2011.06.013 Zhou, 2006, Clusterer ensemble, Knowl.-Based Syst., 19, 77, 10.1016/j.knosys.2005.11.003 Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655 Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 Kim, 2003, Constructing support vector machine ensemble, Pattern Recogn., 36, 2757, 10.1016/S0031-3203(03)00175-4 Cunningham, 2000, Diversity versus quality in classification ensembles based on feature selection, 109 Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647 Yu, 2010, Support vector machine based multiagent ensemble learning for credit risk evaluation, Expert Syst. Appl., 37, 1351, 10.1016/j.eswa.2009.06.083 Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput., 18, 1527, 10.1162/neco.2006.18.7.1527 Mohamed, 2012, Acoustic modeling using deep belief networks, IEEE Trans. Audio Speech Lang. Process., 20, 14, 10.1109/TASL.2011.2109382 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Kim, 2013, Deep learning for robust feature generation in audiovisual emotion recognition, 3687 Haring, 2007, Chromatin immunoprecipitation: optimization, quantitative analysis and data normalization, Plant Methods, 3, 11, 10.1186/1746-4811-3-11 Yang, 2004, Neural network ensembles: combining multiple models for enhanced performance using a multistage approach, Expert Syst., 21, 279, 10.1111/j.1468-0394.2004.00285.x Hopfield, 1982, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci., 79, 2554, 10.1073/pnas.79.8.2554 Hinton, 2012, A practical guide to training restricted Boltzmann machines, 599 El-tawel, 2015, An edge detection scheme based on least squares support vector machine in a contourlet HMT domain, Appl. Soft Comput., 26, 418, 10.1016/j.asoc.2014.10.025 Aburomman, 2016, A novel SVM-kNN-PSO ensemble method for intrusion detection system, Appl. Soft Comput., 38, 360, 10.1016/j.asoc.2015.10.011 Li, 2015, A SVM based classification method for homogeneous data, Appl. Soft Comput., 36, 228, 10.1016/j.asoc.2015.07.027 Xiao, 2016, Ensemble classification based on supervised clustering for credit scoring, Appl. Soft Comput., 43, 73, 10.1016/j.asoc.2016.02.022 Katuwal, 2017, An ensemble of decision trees with random vector functional link networks for multi-class classification, Appl. Soft Comput., 10.1016/j.asoc.2017.09.020 Qiu, 2017, Empirical mode decomposition based ensemble deep learning for load demand time series forecasting, Appl. Soft Comput., 54, 246, 10.1016/j.asoc.2017.01.015 Papa, 2016, Fine-tuning deep belief networks using harmony search, Appl. Soft Comput., 46, 875, 10.1016/j.asoc.2015.08.043 Krawczyk, 2014, Cost-sensitive decision tree ensembles for effective imbalanced classification, Appl. Soft Comput., 14, 554, 10.1016/j.asoc.2013.08.014 Dai, 2015, Class imbalance learning via a fuzzy total margin based support vector machine, Appl. Soft Comput., 31, 172, 10.1016/j.asoc.2015.02.025