1. bookVolumen 6 (2016): Edición 1 (January 2016)
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Recursive-Rule Extraction Algorithm With J48graft And Applications To Generating Credit Scores

Publicado en línea: 13 Jan 2016
Volumen & Edición: Volumen 6 (2016) - Edición 1 (January 2016)
Páginas: 35 - 44
Detalles de la revista
License
Formato
Revista
eISSN
2449-6499
Primera edición
30 Dec 2014
Calendario de la edición
4 veces al año
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Inglés

[1] Marqus, A.I., Garca, V., & Snchez, J.S. On the suitability of resampling techniques for the class imbalance problem in credit scoring. Journal of the Operational Research Society 64, pp. 1060–1070, 2013.Search in Google Scholar

[2] Zhao Z., Xu, S., Kang, B. H., Kabir, M. M. J., & Liu, Y. Investigation and improvement of multilayer perceptron neural networks for credit scoring Expert Systems with Applications 42, pp. 3508–3516, 201510.1016/j.eswa.2014.12.006Search in Google Scholar

[3] Finlay, S. M. Multiple classifier architectures and their applications to credit risk assessment. European Journal of Operational Research 210, pp. 368–378, 2011.10.1016/j.ejor.2010.09.029Search in Google Scholar

[4] Quinlan, J.R. Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning, San Mateo, CA, 1993, Morgan Kaufman.Search in Google Scholar

[5] Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. Comprehensible credit scoring models using support vector machines. European Journal of Operational Research 183, pp. 1497–1488, 2007.Search in Google Scholar

[6] Baesens, B., et al. Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science 49, No. 3, pp. 312–329, 2004.10.1287/mnsc.49.3.312.12739Search in Google Scholar

[7] Abellan, J., and Mantas, C. Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, pp. 3825–3830, 2014.Search in Google Scholar

[8] Finlay, S. M. Multiple classifier architectures and their applications to credit risk assessment. European Journal of Operational Research 210, pp. 368–378, 2011.10.1016/j.ejor.2010.09.029Search in Google Scholar

[9] Braket, N., and Bradely, A.P. Rule extraction from support vector machine: a review. Neurocomputing 74, pp. 178–190, 2010.10.1016/j.neucom.2010.02.016Search in Google Scholar

[10] Setiono, R., and Liu, H. Neurolinear: From neural networks to oblique decision rules. Neurocomputing 17, No. 1, pp. 1–24, 1997.10.1016/S0925-2312(97)00038-6Search in Google Scholar

[11] Setiono, R. and Liu, H. A connectionist approach to generating oblique decision trees. IEEE Trans. Syst., Man, Cybern. B, Cybern. Vol. 29, No. 3, pp. 440–444, Jun. 1999.10.1109/3477.76488018252318Search in Google Scholar

[12] Setiono, R., Baesens, B. & Mues, C. A note on knowledge discovery using neural Setiono networks and its application to credit card screening. European Journal of Operational Research 192, pp.326-332, 2009.10.1016/j.ejor.2007.09.022Search in Google Scholar

[13] Setiono, R., et al. Recursive neural network rule extraction for data with mixed attributes. IEEE Trans. Neural Netw. 19, No. 2, pp. 299–307, 2008.10.1109/TNN.2007.90864118269960Search in Google Scholar

[14] Setiono, R., and Liu, H. Symbolic representation of neural networks. IEEE Computer 29, No. 3, pp. 71–77, 1996.10.1109/2.485895Search in Google Scholar

[15] Gupta, A, Park, S. and Lam, S.M. Generalized analytic rule extraction for feedforward neural networks, IEEE Trans. Knowledge and Data Engineering, 11, pp.985-991, 1999.10.1109/69.824621Search in Google Scholar

[16] Etchell, T.A. and Lisboa, J.P.G., Orthogonal search-based rule extraction (OSRE) for trained neural-networks: A practical and efficient approach, IEEE Trans. Neural Networks 17, pp.374-384, 2006.10.1109/TNN.2005.86347216566465Search in Google Scholar

[17] Hansen, L.K., and Salamon, P., Neural network ensembles. IEEE Trans. Patter Analysis and Machine Intelligence 12, pp. 993–1001, 1990.10.1109/34.58871Search in Google Scholar

[18] Igelnik, S., Pao, Y.-H., LeClair, S. R., and Shen, C. Y. The ensemble approach to neural-network learning and generalization. IEEE Trans. Neural Networks 10, pp. 19–30, 1999.10.1109/72.73749018252500Search in Google Scholar

[19] Liao, J.-J., Shih, C.-H., Chen, T.-F., and Hsu, M.-F. An example-based model for two-class imbalanced financial problem. Economic Modelling 37, pp. 175–183, 2014.10.1016/j.econmod.2013.11.013Search in Google Scholar

[20] Setiono R. et al., Rule extraction from minimal neural networks for credit card screening, Inter. J. of Neural Systems., Vol. 21, No. 4, pp. 265–276, 2011.10.1142/S012906571100282121809474Search in Google Scholar

[21] Setiono R. et al., A note on knowledge discovery using neural networks and its application to credit card screening, European J. Operational Research, Vol. 192, pp. 326–332, 2009.10.1016/j.ejor.2007.09.022Search in Google Scholar

[22] Hayashi Y. et al., Understanding consumer heterogeneity: A business intelligence application of neural networks, Knowledge-Based Systems, Vol. 23, No. 8, pp. 856–863, 2010.10.1016/j.knosys.2010.05.010Search in Google Scholar

[23] Bologna G., Is it worth generating rules from neural network ensemble?, J. of Applied Logic, Vol. 2, pp. 325–348, 2004.10.1016/j.jal.2004.03.004Search in Google Scholar

[24] Zhou, Z.-H. Extracting symbolic rules from trained neural network ensembles. AI Communications 16, pp. 3–15, 2003.Search in Google Scholar

[25] http://fiji.sc/javadoc/weka/classifiers/trees/J48graft.htmlSearch in Google Scholar

[26] Setiono R. et al., A penalty-function approach for pruning feedforward neural networks, Neural Comp., Vol. 9, No. 1, pp. 185–204, 1997.10.1162/neco.1997.9.1.1859117898Search in Google Scholar

[27] Witten, I.H. and Frank, E., Data Ming: Practical Machine Learning Tools With Java Implementations. San Francisco, CA: Morgan Kaufmann, 1999.Search in Google Scholar

[28] Quinlan J.R., Induction of decision trees, Machine Learning, Vol.1, pp.81-106, 1986.10.1007/BF00116251Search in Google Scholar

[29] Webb. G.I., Decision Tree Grafting from the All-Tests-But-One Partition, in Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI), Vol. 2, pp. 702–707, 1999.Search in Google Scholar

[30] Webb, G. I., Decision Tree Grafting, Learining, IJCAI’97 Proceedings of the 15th International Conference on Artificial Intelligence, Vol.2, pp. 846-885, 1997.Search in Google Scholar

[31] Frank, A. & Asuncion, A. University of California. Irvine Machine Learning Repository. http://archive.ics.uci.edu/ml/, 2010Search in Google Scholar

[32] Prechelt, L. Proben1 — A set of benchmarks and benchmarking rules for neural network training algorithms, Technical Report 21/94, Fakultt fr Informatik, Universitt Karlsruhe, Germany. Anonymous ftp available from ftp://pub/papers/techreport/1994/1994-21.ps.gz on ftp.ira.uka.de, 1994.Search in Google Scholar

[33] Smith, M. Neural Networks for Statistical Modeling, New York: Van Nostrand Reinhold, 1993.Search in Google Scholar

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