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Volumn 21, Issue 4, 2011, Pages 265-276

Rule extraction from minimal neural networks for credit card screening

Author keywords

credit scoring; Pruning; rule extraction

Indexed keywords

CLASSIFICATION RULES; CREDIT CARDS; CREDIT SCORING; EFFECTIVE TOOL; HIDDEN UNITS; MODEL SIZE; NETWORK CONNECTION; PREDICTIVE ACCURACY; PREDICTIVE PERFORMANCE; PRUNING; REAL-WORLD PROBLEM; RULE EXTRACTION; RULE SET;

EID: 79961085476     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065711002821     Document Type: Article
Times cited : (38)

References (58)
  • 1
    • 26844455787 scopus 로고    scopus 로고
    • Model selection in Neural Networks: Some difficulties
    • DOI 10.1016/j.ejor.2004.05.026, PII S0377221704004278
    • B. Curry and P. H. Morgan, Model selection in neural networks: Some difficulties, European Journal of Operational Research 170 (2006) 567-577. (Pubitemid 41455535)
    • (2006) European Journal of Operational Research , vol.170 , Issue.2 , pp. 567-577
    • Curry, B.1    Morgan, P.H.2
  • 2
    • 0025751820 scopus 로고
    • Approximation capabilities of multilayer feedforward neural networks
    • K. Hornik, Approximation capabilities of multilayer feedforward neural networks, Neural Networks 4 (1991) 251-257.
    • (1991) Neural Networks , vol.4 , pp. 251-257
    • Hornik, K.1
  • 3
    • 16544370118 scopus 로고    scopus 로고
    • Multilayered greedy network-growing algorithm: Extension of greedy network-growing algorithm to multi-layered networks
    • R. Kamimura, Multilayered greedy network-growing algorithm: Extension of greedy network-growing algorithm to multi-layered networks, Int. Journal of Neural Systems 14(1) 9-26 (2004).
    • (2004) Int. Journal of Neural Systems , vol.14 , Issue.1 , pp. 9-26
    • Kamimura, R.1
  • 4
    • 0001958391 scopus 로고
    • Study of a growth algorithm for a feedforward network
    • [1(1) (1989) 55-59]
    • J.-P. Nadal, Study of a growth algorithm for a feedforward network, Int. Journal of Neural Systems 1(4) (1991) 317-326 [1(1) (1989) 55-59].
    • (1991) Int. Journal of Neural Systems , vol.1 , Issue.4 , pp. 317-326
    • Nadal, J.-P.1
  • 5
    • 1942454824 scopus 로고    scopus 로고
    • New training strategies for constructive neural networks with application to regression problems
    • DOI 10.1016/j.neunet.2004.02.002, PII S0893608004000279
    • L. Ma and K. Khorasani, New training strategies for constructive neural networks with application to regression problems, Neural Networks 17 (2004) 589-609. (Pubitemid 38510341)
    • (2004) Neural Networks , vol.17 , Issue.4 , pp. 589-609
    • Ma, L.1    Khorasani, K.2
  • 6
    • 37249029174 scopus 로고    scopus 로고
    • A hybrid forward algorithm for RBF neural network construction
    • DOI 10.1109/TNN.2006.880860
    • J.-X. Peng, K. Li and D.-S. Huang, A hybrid forward algorithm for RBF neural network construction, IEEE Transactions on Neural Networks 19(2) (2006) 1439-1451. (Pubitemid 44824258)
    • (2006) IEEE Transactions on Neural Networks , vol.17 , Issue.6 , pp. 1439-1451
    • Peng, J.-X.1    Li, K.2    Huang, D.-S.3
  • 7
    • 0029185114 scopus 로고
    • Use of a quasi Newton method in a feedforward neural network construction algorithm
    • R. Setiono and L. C. K. Hui, Use of a quasi Newton method in a feedforward neural network construction algorithm, IEEE Transactions on Neural Networks 6(2) (1995) 326-332.
    • (1995) IEEE Transactions on Neural Networks , vol.6 , Issue.2 , pp. 326-332
    • Setiono, R.1    Hui, L.C.K.2
  • 8
    • 24344455644 scopus 로고    scopus 로고
    • Sensitivity analysis applied to the construction of radial basis function networks
    • DOI 10.1016/j.neunet.2005.02.006, PII S0893608005000547
    • D. Shi, D. S. Yeung and J. Gao, Sensitivity analysis applied to the construction of radial basis function networks, Neural Networks 18 (2005) 951-957. (Pubitemid 41253510)
    • (2005) Neural Networks , vol.18 , Issue.7 , pp. 951-957
    • Shi, D.1    Yeung, D.S.2    Gao, J.3
  • 9
    • 0035505658 scopus 로고    scopus 로고
    • A new pruning heuristic based on variance analysis of sensitivity information
    • A. Engelbrecht, A new pruning heuristic based on variance analysis of sensitivity information, IEEE Transactions on Neural Networks 12(6) (2001) 1386-1399.
    • (2001) IEEE Transactions on Neural Networks , vol.12 , Issue.6 , pp. 1386-1399
    • Engelbrecht, A.1
  • 10
    • 33644884686 scopus 로고    scopus 로고
    • A node pruning agorithm based on a fourier amplitude sensitivity test method
    • DOI 10.1109/TNN.2006.871707
    • P. Lauret, E. Fock and T. A. Mara, A node pruning algorithm based on a fourier amplitude sensitivity test method, IEEE Transactions on Neural Networks 17(2) (2006) 273-293. (Pubitemid 43380055)
    • (2006) IEEE Transactions on Neural Networks , vol.17 , Issue.2 , pp. 273-293
    • Lauret, P.1    Fock, E.2    Mara, T.A.3
  • 11
    • 33750378348 scopus 로고    scopus 로고
    • A new training and pruning algorithm based on node dependence and Jacobian rank deficiency
    • DOI 10.1016/j.neucom.2005.11.005, PII S0925231206000075
    • J. Xu and D. W. C. Ho, A new training and pruning algorithm based on node dependence and Jacobian rank deficiency, Neurocomputing 70 (2006) 544-558. (Pubitemid 44615765)
    • (2006) Neurocomputing , vol.70 , Issue.1-3 , pp. 544-558
    • Xu, J.1    Ho, D.W.C.2
  • 12
    • 32544452874 scopus 로고    scopus 로고
    • Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure
    • DOI 10.1016/j.neucom.2005.04.010, PII S0925231205001852
    • X. Zeng and D. S. Yeung, Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure, Neurocomputing 69 (2006) 825-837. (Pubitemid 43230385)
    • (2006) Neurocomputing , vol.69 , Issue.7-9 SPEC. ISS. , pp. 825-837
    • Zeng, X.1    Yeung, D.S.2
  • 13
    • 13844256702 scopus 로고    scopus 로고
    • A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
    • DOI 10.1109/TNN.2004.836241
    • D. Huang, P. Saratchandran and N. Sundararajan, A generalized growing and pruning RBF (GGAPRBF) neural network for function approximation, IEEE Transactions on Neural Networks 16(1) (2005) 57-67. (Pubitemid 40241910)
    • (2005) IEEE Transactions on Neural Networks , vol.16 , Issue.1 , pp. 57-67
    • Huang, G.-B.1    Saratchandran, P.2    Sundararajan, N.3
  • 14
  • 15
    • 0042525842 scopus 로고    scopus 로고
    • Neural-network construction and selection in nonlinear modeling
    • I. Rivals and L. Personnaz, Neural-network construction and selection in nonlinear modeling, IEEE Transactions on Neural Networks 14(4) (2003) 804-819.
    • (2003) IEEE Transactions on Neural Networks , vol.14 , Issue.4 , pp. 804-819
    • Rivals, I.1    Personnaz, L.2
  • 16
    • 13844298045 scopus 로고    scopus 로고
    • Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
    • DOI 10.1109/TNN.2004.841414
    • T. W. S. Chow and D. Huang, Estimating optimal feature subsets using efficient estimation of highdimensional mutual information, IEEE Transactions on Neural Networks 16(1) (2005) 213-224. (Pubitemid 40241922)
    • (2005) IEEE Transactions on Neural Networks , vol.16 , Issue.1 , pp. 213-224
    • Chow, T.W.S.1    Huang, D.2
  • 17
    • 0030129019 scopus 로고    scopus 로고
    • Improving backpropagation learning with feature selection
    • R. Setiono and H. Liu, Improving backpropagation learning with feature selection, Applied Intelligence 6(2) (1996) 129-140.
    • (1996) Applied Intelligence , vol.6 , Issue.2 , pp. 129-140
    • Setiono, R.1    Liu, H.2
  • 18
    • 59349101361 scopus 로고    scopus 로고
    • Feature selection in bankruptcy prediction
    • C.-F. Tsai, Feature selection in bankruptcy prediction, Knowledge Based Systems 22(2) (2009) 120-127.
    • (2009) Knowledge Based Systems , vol.22 , Issue.2 , pp. 120-127
    • Tsai, C.-F.1
  • 19
    • 77958043391 scopus 로고    scopus 로고
    • Variable selection in nonlinear modeling based on RBF networks and evolutionary computation
    • P. Patrinos, A. Alexandridis, K. Ninos and H. Sarimveis, Variable selection in nonlinear modeling based on RBF networks and evolutionary computation, Int. Journal of Neural Systems 20(5) (2010) 365-379.
    • (2010) Int. Journal of Neural Systems , vol.20 , Issue.5 , pp. 365-379
    • Patrinos, P.1    Alexandridis, A.2    Ninos, K.3    Sarimveis, H.4
  • 20
    • 0034061686 scopus 로고    scopus 로고
    • Variable selection using neural-network models
    • DOI 10.1016/S0925-2312(99)00146-0, PII S0925231299001460
    • G. Castellano and A. M. Fanelli, Variable section using neural-network models, Neurocomputing 31 (2000) 1-13. (Pubitemid 30149404)
    • (2000) Neurocomputing , vol.31 , Issue.1-4 , pp. 1-13
    • Castellano, G.1    Fanelli, A.M.2
  • 21
    • 49449083313 scopus 로고    scopus 로고
    • Feature selection using localized generalization error for supervised classification problems using RBFNN
    • W. W. Y. Ng, D. S. Yeung, M. Firth, E. C. C. Tsang and X.-Z. Wang, Feature selection using localized generalization error for supervised classification problems using RBFNN, Pattern Recognition 41 (2008) 3706-3719.
    • (2008) Pattern Recognition , vol.41 , pp. 3706-3719
    • Ng, W.W.Y.1    Yeung, D.S.2    Firth, M.3    Tsang, E.C.C.4    Wang, X.-Z.5
  • 22
    • 0031140388 scopus 로고    scopus 로고
    • Neural-network feature selector
    • PII S1045922797027628
    • R. Setiono and H. Liu, Neural-network feature selector, IEEE Transactions on Neural Networks 8(7) (1997) 654-662. (Pubitemid 127767809)
    • (1997) IEEE Transactions on Neural Networks , vol.8 , Issue.3 , pp. 654-662
    • Setiono, R.1    Liu, H.2
  • 23
    • 0030633575 scopus 로고    scopus 로고
    • A penalty-function approach for pruning feedforward neural networks
    • R. Setiono, A penalty-function approach for pruning feedforward neural networks, Neural Computation 9(1) (1997) 185-204. (Pubitemid 127622532)
    • (1997) Neural Computation , vol.9 , Issue.1 , pp. 185-204
    • Setiono, R.1
  • 24
    • 0025447562 scopus 로고
    • A simple procedure for pruning back-propagation trained neural networks
    • E. D. Karnin, A simple procedure for pruning back-propagation trained neural networks, IEEE Transactions on Neural Networks 1(2) (1990) 239-242.
    • (1990) IEEE Transactions on Neural Networks , vol.1 , Issue.2 , pp. 239-242
    • Karnin, E.D.1
  • 25
    • 0001037725 scopus 로고
    • Improving generalization of neural networks through pruning
    • H. H. Thodberg, Improving generalization of neural networks through pruning, Int. Journal of Neural Systems 1(4) (1991) 317-326.
    • (1991) Int. Journal of Neural Systems , vol.1 , Issue.4 , pp. 317-326
    • Thodberg, H.H.1
  • 27
    • 0028480401 scopus 로고
    • Two original weight pruning methods based on statistical tests and rounding techniques
    • G. Ledoux and J. F. Grandin, Two original weight pruning methods based on statistical tests and rounding techniques, in IEE Proc. Vision, Image and Signal Processing 141(4) (1994) 230-237.
    • (1994) IEE Proc. Vision, Image and Signal Processing , vol.141 , Issue.4 , pp. 230-237
    • Ledoux, G.1    Grandin, J.F.2
  • 28
    • 55849084307 scopus 로고    scopus 로고
    • Pruning artificial neural networks using neural complexity measures
    • T. D. Jorgensen, B. P. Haynes and C. C.F Norlund, Pruning artificial neural networks using neural complexity measures, Int. Journal of Neural Systems 18(5) (2008) 389-403.
    • (2008) Int. Journal of Neural Systems , vol.18 , Issue.5 , pp. 389-403
    • Jorgensen, T.D.1    Haynes, B.P.2    Norlund, C.C.F.3
  • 29
    • 1542435014 scopus 로고    scopus 로고
    • Entropy learning and relevance criteria for neural network pruning
    • G. S. Ng, A. Wahab and D. Shi, Entropy learning and relevance criteria for neural network pruning, Int. Journal of Neural Systems 13(5) (2003) 291-305.
    • (2003) Int. Journal of Neural Systems , vol.13 , Issue.5 , pp. 291-305
    • Ng, G.S.1    Wahab, A.2    Shi, D.3
  • 30
    • 55349138530 scopus 로고    scopus 로고
    • UCI Repository of machine learning databases
    • University of California. Available from
    • A. Asuncion and D. J. Newman, UCI Repository of machine learning databases. Irvine, CA: School of of Information and Computer Sciences, University of California. Available from http://www.ics.uci.edu/mlearn/ MLRepository.html (2007).
    • (2007) Irvine CA: School of of Information and Computer Sciences
    • Asuncion, A.1    Newman, D.J.2
  • 31
    • 0004114283 scopus 로고
    • Technical Report 21/94, Fakultät für Informatik, Universität Karlsruhe, Germany, Anonymous ftp available from
    • L. Prechelt, Proben1 - A set of benchmarks and benchmarking rules for neural network training algorithms, Technical Report 21/94, Fakultät für Informatik, Universität Karlsruhe, Germany. Anonymous ftp available from ftp://pub/papers/techreports/1994/1994-21.ps.gz on ftp.ira.uka.de (1994).
    • (1994) Proben1 - A Set of Benchmarks and Benchmarking Rules for Neural Network Training Algorithms
    • Prechelt, L.1
  • 32
    • 0037534150 scopus 로고    scopus 로고
    • Using neural network rule extraction and decision tables for credit-risk evaluation
    • B. Baesens, R. Setiono, C. Mues,C. and J. Vanthienen, Using neural network rule extraction and decision tables for credit-risk evaluation, Management Science 49(3) (2003) 312-329.
    • (2003) Management Science , vol.49 , Issue.3 , pp. 312-329
    • Baesens, B.1    Setiono, R.2    MuesC, C..3    Vanthienen, J.4
  • 33
    • 50849098868 scopus 로고    scopus 로고
    • A note on knowledge discovery using neural networks and its application to credit screening
    • R. Setiono, B. Baesens and C. Mues, A note on knowledge discovery using neural networks and its application to credit screening, European Journal of Operational Research 192(1) (2009) 1009-1018.
    • (2009) European Journal of Operational Research , vol.192 , Issue.1 , pp. 1009-1018
    • Setiono, R.1    Baesens, B.2    Mues, C.3
  • 34
    • 25144464662 scopus 로고    scopus 로고
    • Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem
    • DOI 10.1016/j.ejor.2004.05.018, PII S0377221704003984, Balancing Assembly and Transfer Lines
    • R. S. Sexton, S. McMurtrey and D. J. Cleavenger, Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem, European Journal of Operational Research 168 (2006) 1009-1018. (Pubitemid 41336607)
    • (2006) European Journal of Operational Research , vol.168 , Issue.3 , pp. 1009-1018
    • Sexton, R.S.1    McMurtrey, S.2    Cleavenger, D.3
  • 35
    • 2342472859 scopus 로고    scopus 로고
    • A study on rule extraction from several combined neural networks
    • G. Bologna, A study on rule extraction from several combined neural networks, Int. Journal of Neural Systems 11(3) (2001) 247-255.
    • (2001) Int. Journal of Neural Systems , vol.11 , Issue.3 , pp. 247-255
    • Bologna, G.1
  • 36
    • 30344459717 scopus 로고    scopus 로고
    • Fuzzy rule extraction from a feed forward neural network by training a representative fuzzy neural network using gradient descent
    • DOI 10.1142/S0218488505003746
    • R. K. Brouwer, Fuzzy rule extraction from a feed forward neural network by training a representative fuzzy neural network using gradient descent, Int. Journal of Uncertainty, Fuzziness and Knowledge- Based Systems 13(6) (2005) 673-698. (Pubitemid 43068929)
    • (2005) International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems , vol.13 , Issue.6 , pp. 673-698
    • Brouwer, R.K.1
  • 37
    • 78649583381 scopus 로고    scopus 로고
    • Knowledge extraction from evolving spiking neural networks with rank order population coding
    • S. Soltic and N. Kasabov, Knowledge extraction from evolving spiking neural networks with rank order population coding, Int. Journal of Neural Systems 20(6) (2010) 437-445.
    • (2010) Int. Journal of Neural Systems , vol.20 , Issue.6 , pp. 437-445
    • Soltic, S.1    Kasabov, N.2
  • 38
    • 40549122717 scopus 로고    scopus 로고
    • Recursive neural network rule extraction for data with mixed attributes
    • DOI 10.1109/TNN.2007.908641
    • R. Setiono, B. Baesens and C. Mues, Recursive neural network rule extraction for data with mixed attributes, IEEE Transactions on Neural Networks 19(2) (2008) 299-307. (Pubitemid 351359294)
    • (2008) IEEE Transactions on Neural Networks , vol.19 , Issue.2 , pp. 299-307
    • Setiono, R.1    Baesens, B.2    Mues, C.3
  • 44
    • 0030291564 scopus 로고    scopus 로고
    • A comparison of neural networks and linear scoring models in the credit union environment
    • DOI 10.1016/0377-2217(95)00246-4
    • V. S. Desai, J. N. Crook and G. A. Overstreet Jr, A comparison of neural networks and linear scoring models in the credit union environment, European Journal of Operational Research 95(1) (1996) 24-37. (Pubitemid 126391970)
    • (1996) European Journal of Operational Research , vol.95 , Issue.1 , pp. 24-37
    • Desai, V.S.1    Crook, J.N.2    Overstreet Jr., G.A.3
  • 45
    • 70249110487 scopus 로고    scopus 로고
    • A neural network model for credit risk evaluation
    • A. Khashman, A neural network model for credit risk evaluation, Int. Journal of Neural Systems 19(4) (2009) 285-294.
    • (2009) Int. Journal of Neural Systems , vol.19 , Issue.4 , pp. 285-294
    • Khashman, A.1
  • 48
    • 33646023117 scopus 로고    scopus 로고
    • An introduction to ROC analysis
    • T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters 27 (2006) 861-874.
    • (2006) Pattern Recognition Letters , vol.27 , pp. 861-874
    • Fawcett, T.1
  • 49
    • 70249110487 scopus 로고    scopus 로고
    • A neural network model for credit risk evaluation
    • A. Khasnan, A neural network model for credit risk evaluation, Int. Journal of Neural Systems 19(4) (2009) 285-294.
    • (2009) Int. Journal of Neural Systems , vol.19 , Issue.4 , pp. 285-294
    • Khasnan, A.1
  • 51
    • 0027580356 scopus 로고    scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • R. C. Holte, Very simple classification rules perform well on most commonly used datasets, Machine Learning 11(1) (2003) 63-90.
    • (2003) Machine Learning , vol.11 , Issue.1 , pp. 63-90
    • Holte, R.C.1
  • 52
    • 0038636391 scopus 로고    scopus 로고
    • A comparative assessment of classification methods
    • M. Y. Kiang, A comparative assessment of classification methods, Decision Support Systems 35(4) (2003) 441-454.
    • (2003) Decision Support Systems , vol.35 , Issue.4 , pp. 441-454
    • Kiang, M.Y.1
  • 53
    • 79961071682 scopus 로고    scopus 로고
    • Computational intelligence methods for rule-based data understanding
    • W. Duch, R. Setiono and J. Zurada, Computational intelligence methods for rule-based data understanding, Proceedings of the IEEE 32(3) (2004) 251-270.
    • (2004) Proceedings of the IEEE , vol.32 , Issue.3 , pp. 251-270
    • Duch, W.1    Setiono, R.2    Zurada, J.3
  • 55
    • 0025508828 scopus 로고
    • Predicting bank failure: A neural network approach
    • K.-Y. Tam and M. Kiang, Predicting bank failure: A neural network approach, Applied Artificial Intelligence 4 (1990) 265-282.
    • (1990) Applied Artificial Intelligence , vol.4 , pp. 265-282
    • Tam, K.-Y.1    Kiang, M.2
  • 56
    • 64049084825 scopus 로고    scopus 로고
    • Predicting business failure using multiple case-based reasoning combined with support vector machine
    • H. Li and J. Sun, Predicting business failure using multiple case-based reasoning combined with support vector machine, Expert Systems with Application 36 (2009) 10085-10096.
    • (2009) Expert Systems with Application , vol.36 , pp. 10085-10096
    • Li, H.1    Sun, J.2
  • 57
    • 16244399834 scopus 로고    scopus 로고
    • Predicting bond ratings using publicly available information
    • DOI 10.1016/j.eswa.2005.01.007, PII S0957417405000084
    • K. S. Kim, Predicting bond rating using publicly available information, Expert Systems with Application 29(1) (2005) 75-81. (Pubitemid 40454394)
    • (2005) Expert Systems with Applications , vol.29 , Issue.1 , pp. 75-81
    • Kim, K.S.1
  • 58
    • 2442655599 scopus 로고    scopus 로고
    • Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis
    • M. Lam, Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis, Decision Support Systems 37(4) (2004) 567-581.
    • (2004) Decision Support Systems , vol.37 , Issue.4 , pp. 567-581
    • Lam, M.1


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