Dr. Rudy Setiono

Publications

In this page: Journal publications | Conference publications (since 1995) | Book chapters

Journal publications

  1. A. Azcarraga and R. Setiono. Neural network rule extraction for gaining insight into the characteristics of poverty, Neural Computing and Applications, pages 1-12, 2017.
  2. Y. Hayashi, R. Setiono and A. Azcarraga. Neural network training and rule extraction with augmented discretized input, Neurocomputing, Vol. 207, pages 610-622, 2016.
  3. R. Setiono, A. Azcarraga and Y. Hayashi. Using sample selection to improve accuracy and simplicity of rules extracted from neural networks for credit scoring applications, International Journal of Computational Intelligence and Applications, Vol. 14, No. 4, pages 1550021-1-20, 2015.
  4. R. Setiono, B. Baesens and C. Mues. Rule extraction from minimal neural networks for credit card screening, International Journal of Neural Systems, Vol. 21, No. 4, pages 265-276, 2011.
  5. Y. Hayashi, M-H. Hsieh and R. Setiono. Understanding consumer heterogeneity: A business intelligence application of neural networks, Knowledge Based Systems, Vol. 23, No. 8, pages 856-863, 2010.
  6. Y. Hayashi, M-H. Hsieh and R. Setiono. Predicting consumer preference for fast-food franchise: A data mining approach, Journal of the Operational Research Society, Vol. 60, No. 9, pages 1221-1229, 2009.
  7. 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, Vol. 192, No. 1, pages 326-332, 2009.
  8. K. Odajima, Y. Hayashi, T. Gong and R. Setiono. Greedy rule generation from discrete data and its use in neural network rule extraction, Neural Networks, Vol. 21, No. 7, pages 1020-1028, 2008.
  9. J. Huysmans, R. Setiono, B. Baesens and J. Vanthienen. Minerva: sequential covering for rule generation, IEEE Transactions on Systems, Man and Cybernetics Part B, Vol. 38, No. 2, pages 299-309, 2008.
  10. R. Setiono, B. Baesens and C. Mues. Recursive neural network rule extraction for data with mixed attributes, IEEE Transactions on Neural Networks, Vol. 19, No. 2, pages 299-307, 2008.
  11. R. Setiono, S.L. Pan, M.H. Hsieh and A. Azcarraga. Knowledge acquisition and revision using neural networks: an application to a cross-national study of brand image perception, Journal of the Operational Research Society, Vol. 57, No. 3, pages 231-240, 2006.
  12. A. Azcarraga, M.H. Hsieh, S.L. Pan and R. Setiono. Extracting salient dimensions for automatic SOM labeling, IEEE Transactions on Systems, Man, and Cybernetics, Part C, Vol. 35, No. 4, pages 595-600, 2005.
  13. R. Setiono, S.L. Pan, M.H. Hsieh and A. Azcarraga. Separating core and non-core knowledge: An application of neural network rule extraction to a cross-national study of brand image perception, IEEE Transactions on Systems, Man, and Cybernetics, Part C, Vol. 35, No. 4, pages 465-475, 2005.
  14. R. Setiono, S.L. Pan, M.H. Hsieh and A. Azcarraga. Automatic knowledge extraction from survey data: learning M-of-N constructs using a hybrid approach, Journal of the Operational Research Society, Vol. 56, No. 1, pages 3-14, 2005.
  15. W. Wu, X. Liu, M. Xu, J. Peng and R. Setiono. A hybrid SOM-SVM approach for the zebra fish genome analysis, Genomics Proteomic & Bioinformatics, Vol. 3, No. 2, pages 84-93, 2005.
  16. M.H. Hsieh, S.L. Pan and R. Setiono. Product-, corporate-, and country-image dimensions and purchase behavior: A multicountry analysis, Journal of the Academy of Marketing Science, Vol. 32, No. 3, pages 251-270, 2004.
  17. W. Duch, R. Setiono and J. Zurada. Computational intelligence methods for rule-based data understanding, Proceedings of the IEEE, Vol. 92, No. 5, pages 771-805, 2004.        
  18. R. Setiono and J. Thong. An approach to generate rules from neural networks for regression problems, European Journal of Operational Research, Vol. 155, No. 1, pages 239-250, 2004.
  19. M. Xu and R. Setiono. Gene selection for cancer classification using a hybrid of univariate and multivariate feature selection methods, Applied Genomics and Proteomics, Vol. 2, No. 2, 2003, pages 79-91.
  20. B. Baesens, R. Setiono, C. Mues and J. Vanthienen. Using neural network rule extraction and decision tables for credit risk evaluation, Management Science, Vol. 49, No. 3, 2003, pages 312-329.
  21. R. Setiono and A. Azcarraga. Generating concise sets of linear regression rules from artificial neural networks, Journal on Artificial Intelligence Tools, Vol. 11, No. 2, 2002, pages 189-202.
  22. Y. Hayashi and R. Setiono. Combining neural network predictions for medical diagnosis, Computers in Biology and Medicine, Vol. 32, 2002, pages 237-246.
  23. R. Setiono, W.K. Leow and J.M. Zurada. Extraction of rules from artificial neural networks for nonlinear regression, IEEE Transactions on Neural Networks, 2002, Vol. 13, No. 3, pages 564-577.
  24. H. Lu and R. Setiono. Effective query size estimation using neural networks, Journal of Applied Intelligence, 2002, Vol. 16, No. 3, pages 173-184.
  25. R. Setiono. Feedforward neural network construction using cross-validation, Neural Computation, 2001, Vol. 13, No. 12, pages 2865-2877.
  26. Y. Hayashi, R. Setiono and K. Yoshida. Learning M-of-N concepts for medical diagnosis using neural networks, Journal of Advanced Computational Intelligence, 2000, Vol. 4. No. 4, pages 294-301.
  27. Y. Hayashi, R. Setiono and K. Yoshida. A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders, Artificial Intelligence in Medicine, 2000, Vol. 20, pages 205-216.
  28. R. Setiono. Extracting M-of-N rules from trained neural networks, IEEE Transactions on Neural Networks, 2000, Vol. 11, No. 2, pages 512-519.
  29. R. Setiono. Generating concise and accurate classification rules for breast cancer diagnosis, Artificial Intelligence in Medicine, 2000, Vol 18, No. 3, pages 205-219.
  30. R. Setiono and W.K. Leow. FERNN: An algorithm for Fast Extraction of Rules from Neural Networks, Journal of Applied Intelligence, 2000, Vol. 12, No. 1/2, pages 15-25.
  31. R. Setiono and W.K. Leow. On mapping decision trees and neural networks, Knowledge Based Systems, 1999, Vol. 12, No. 3, pages 95-99.
  32. R. Setiono and H. Liu. A connectionist approach to generating oblique decision trees, IEEE Transactions on Systems, Man, and Cybernetics, 1999, Vol. 29, No. 3, pages 440-444.
  33. W.K. Leow and R. Setiono. Explanation of the "Virtual Input" Phenomenon, Neural Networks, 1999, Vol. 12, pages 191-192.
  34. H. Liu and R. Setiono. Some issues on scalable feature selection, Expert Systems with Application, 1998, Vol. 15, pages 333-339.
  35. R. Setiono, J.Y.L. Thong and C. Yap. Symbolic rule extraction from neural networks: An application to identifying organizations adopting IT, Information and Management, 1998, Vol. 34, No. 2, pages 91-101.
  36. H. Liu and R. Setiono. Incremental feature selection, Journal of Applied Intelligence, 1998, Vol. 9, No. 3, pages 217-230.
  37. R. Setiono and H. Liu. Analysis of hidden representations by greedy clustering, Connection Science, Vol. 10, No. 1, 1998, pages 21-42.
  38. R. Setiono and H. Liu. NeuroLinear: from neural networks to oblique decision rules, Neurocomputing, Vol. 17, No. 1, September 1997, pages 1-24.
  39. R. Setiono. On the solution of the parity problem by a single hidden layer feedforward neural network, Neurocomputing, Vol. 16, No. 3, September 1997, pages 225-235.
  40. H. Liu and R. Setiono. Feature selection via discretization of numeric attributes, IEEE Transactions on Knowledge and Data Engineering, Vol. 9, No. 4, July/August 1997, pages 642-645.
  41. R. Setiono and H. Liu. Neural-network feature selector, IEEE Transactions on Neural Networks, Vol. 8, No. 3, May 1997, pages 654-662.
  42. R. Setiono. Extracting rules from neural networks by pruning and hidden-unit splitting, Neural Computation, Vol. 9, No. 1, January 1997, pages 205-225.
  43. R. Setiono. A penalty-function approach for pruning feedforward neural networks, Neural Computation, Vol. 9, No. 1, January 1997, pages 185-204.
  44. H. Lu, R. Setiono and H. Liu. Effective data mining using neural networks, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, December 1996, pages 957-961.
  45. R. Setiono and H. Liu. Improving backpropagation learning with feature selection, Journal of Applied Intelligence, Vol. 6, No. 2, April 1996, pages 129-140.
  46. R. Setiono and H. Liu. Symbolic representation of neural networks, IEEE Computer, Vol. 29, No. 3, March 1996, pages 71-77.
  47. H. Liu and R. Setiono. Dimensionality reduction via discretization, Knowledge Based Systems, Vol. 9, No. 1, February 1996, pages 67-72.
  48. R. Setiono. Extracting rules from pruned neural networks for breast cancer diagnosis, Artificial Intelligence in Medicine, Vol. 8, No. 1, February 1996, pages 37-51.
  49. Y. Gao and R. Setiono. Design of a feedforward neural network construction algorithm and its implementation on scalable parallel machine, Journal of Japanese Society of Artificial Intelligence, Vol. 10, No. 4, July 1995, pages 580-589.
  50. R. Setiono. A neural network construction algorithm which maximizes the likelihood function, Connection Science, Vol. 7, No. 2, 1995, pages 147-166.
  51. R. Setiono and L.C.K. Hui. Use of quasi-Newton method in a feedforward neural network construction algorithm, IEEE Transactions on Neural Networks, Vol. 6, No. 1, 1995, pages 273-277.
  52. S.L. Chung and R. Setiono. Efficient neural network training on a Cray Y-MP, International Journal of High Speed Computing, Vol. 7, No. 1, 1995, pages 109-124.
  53. R. Setiono and G. Lu. A neural network construction algorithm with application to image compression, Neural Computing & Applications, Vol. 2, No. 2, 1994, pages 61-68.
  54. R. Setiono. Interior dual proximal point algorithm for linear programs, European Journal of Operational Research, Vol. 77, 1994, pages 96-110.
  55. R. Setiono. Interior dual least 2-norm algorithm for linear programs, SIAM Journal on Control and Optimization, Vol. 31, No. 4, 1993, pages 875-899.
  56. R. Setiono. Interior dual proximal point algorithm using preconditioned conjugate gradient. Optimization, Vol. 24, 1992, pages 63-73.
  57. R. Setiono. Interior proximal point algorithm for linear programs. Journal of Optimization Theory and Application, Vol. 74, No. 3, 1992, pages 425-444.

Conference publications (since 1995)

  1. A. Azcarraga, A. Caronongan, R. Setiono and S. Manalili. Validating the stable clustering of songs in a structured 3D SOM. In Proceedings of IJCNN 2016, International Joint Conference on Neural Networks, Vancouver, Canada, July 2016.
  2. R. Setiono. Determining relevant variables and interactions in credit scoring data with neural network pruning and rule extraction. In Proceedings of the 45th International Decision Sciences Institute Conference, Tampa, USA, November 2014.
  3. A. Azcarraga, P. Tensuan and R. Setiono. Tagging documents using neural networks based on local word features. In Proceedings of IJCNN 2014, International Joint Conference on Neural Networks, Beijing, China, July 2014, pages 724-731.
  4. R. Setiono, A. Azcarraga, and Y. Hayashi. MofN rule extraction from neural networks trained with discretized input. In Proceedings of IJCNN 2014, International Joint Conference on Neural Networks, Beijing, China, July 2014, pages 1079-1086.
  5. J. Hartanto and R. Setiono. Combining Chi2 discretization and neural networks for efffective predictive analytics. In Proceedings of the 44th International Decision Sciences Institute Conference, Baltimore, USA, November 2013.
  6. A. Azcarraga, Y. Hayashi and R. Setiono. Credit scoring using neural networks with augmented discretized inputs. In Proceedings of the 12th International Decision Sciences Institute Conference, Bali, Indonesia, July 2013, pages 1412-1426.
  7. A. Azcarraga, C. Enriquez, Y. Hayashi and R. Setiono. Using neural network for visualizing poverty. In Proceedings of the 12th International Decision Sciences Institute Conference, Bali, Indonesia, July 2013, pages 1005-1023.
  8. R. Setiono. Sample selection and neural network rule extraction for credit scoring. In Proceedings of the 43rd Annual Meeting of the Decision Sciences Institute, San Francisco, USA, November 2012.
  9. R. Setiono and A. Seret. Discrete variable generation for improved neural network classification. In Proceedings of the 24th International Conference on Tools with Artificial Intelligence, Athens, Greece, November 2012, pages 230-237.
  10. A. Azcarraga, M. D. Liu and R. Setiono. Keyword extraction using backpropagation neural networks and rule extraction. In Proceedings of IJCNN 2012, International Joint Conference on Neural Networks, Brisbane, Australia, June 2012, pages 1-7.
  11. Y. Hayashi, R. Setiono and M.H. Hsieh. Maximizing the area under ROC using pruned neural networks and its application to analyzing market survey data. In Proceedings of the 42nd Annual Meeting of the Decision Sciences Institute, Boston, USA, November 2011, pages 2161-2166.
  12. B. Baesens, R. Setiono, C. Mues and T. Van Gestel. Neural network rule extraction and decision tables for software fault prediction. In Proceedings of the Workshop on Use of Risk Analysis in Computer-aided Perusasion, The NATO Science for Peace and Security Programme, Antalya, Turkey, 2011.
  13. H. Liu , H. Motoda, R. Setiono and Z. Zhao. Feature Selection: An Ever Evolving Frontier in Data Mining. In Journal of Machine Learning Research - Proceedings Track, 10: 4-13, 2010.
  14. R. Setiono, K. Dejaeger, W. Verbeke, D. Martens and B. Baesens. Software effort prediction using regression rule extraction from neural networks. In Proceedings of the 22th International Conference on Tools with Artificial Intelligence, Arras, France, October 2010.
  15. R. Setiono and M. Tanaka. Neural network rule extraction and the LED display recognition problem. In Proceedings of the 22th International Conference on Tools with Artificial Intelligence, Arras, France, October 2010.
  16. Y. Hayashi, M.H. Hsieh and R. Setiono. Predicting eating-out frequency of Taiwanese consumers: A business intelligence application of neural networks. In Proceedings of the 10th International Conference Decision Sciences Institute, Nancy, France, June 2009, pages 651-670.
  17. A. Azcarraga, M.H. Hsieh and R. Setiono. Market research application of neural networks. In Proceedings of CEC 2008, IEEE Congress on Evolutionary Computing, Hongkong, China, June 2008, pages 357-363.
  18. B.Baesens, R. Setiono and C. Mues. Neural network rule extraction and decision tables for software fault prediction. In Proceedings of ICONIP 2007, International Conference on Neural and Information Processing, Kitakyushu, Japan, November 2007.
  19. R. Setiono, B. Baesens and C. Mues. Risk management and regulatory compliance:A data mining framework based on neural network rule extraction. In Proceedings of ICIS 2006, International Conference on Information Systems, Milwaukee, USA, December 2006.
  20. K. Odajima, Y. Hayashi and R. Setiono. Greedy rule generation from discrete data and its use in neural network rule extraction. In Proceedings of IJCNN 2006, International Joint Conference on Neural Networks, Vancouver, Canada, July 2006, pages 3499-3505.
  21. T. Huynh and R. Setiono. Effective neural network pruning using cross-validation. In Proceedings of IJCNN 2005, International Joint Conference on Neural Networks, Montreal, Canada, July-August 2005, pages 972-977.
  22. A. Azcarraga, M.H. Hsieh and R. Setiono. Car market segmentation using SOM and decision trees. In Proceedings of the 8th International Conference of DSI, Barcelona, Spain, July 2005, pages 457-465.
  23. V. Tam, R. Setiono and A. Santoso. Applying the conjugate gradient method for text document categorization. In Proceedings of the ICPR 2004, the 17th International Conference on Pattern Recognition, Cambridge, UK, August 2004.
  24. W. Wu, X. Liu, M. Xu, J. Peng and R. Setiono. A hybrid SOM-SVM method for analyzing zebra fish gene expression. In Proceedings of the ICPR 2004, the 17th International Conference on Pattern Recognition, Cambridge, UK, August 2004.
  25. A. Azcarraga, M. Hsieh, S.L. Pan and R. Setiono. Knowledge acquisition and revision using neural networks. In Proceedings of IJCNN 2004, International Joint Conference on Neural Networks, Budapest, Hungary, July 2004, pages 1365-1370.
  26. A. Azcarraga, M. Hsieh and R. Setiono. Visualizing globalization: A SOM approach to customer profiling. In Proceedings of ICIS 2003, International Conference on Information Systems, Seattle, USA, December 2003, pages 592-603.
  27. B. Baesens, C. Mues, M. Backer, J. Vanthienen and R. Setiono. Building intelligent credit scoring systems using decision tables. In Proceedings of ICEIS 2003, International Conference on Enterprise Information Systems, Angers, France, April 2003.
  28. V. Tam, A. Santoso and R. Setiono. A comparative study of centroid-based, neighborhood-based and statistical approaches for effective document categorization. In Proceedings of the 16th International Conference on Pattern Recognition, ICPR 2002, Vol. 4 Quebec, Canada, August 2002, pages 235-238.
  29. B. Baesens, R. Setiono, C. Mues, S. Viane and J. Vanthienen. Building credit scoring expert systems using neural network rule extraction and decision tables. In Proceedings of ICIS 2001, International Conference on Information Systems, New Orleans, USA, December 2001.
  30. A. Gaweda, J. Zurada and R. Setiono. Input selection in data-driven fuzzy modeling. In Proceedings of 2001 IEEE International Fuzzy Systems Conference, Melbourne, Australia, December 2001.
  31. R. Setiono and A. Azcarraga. An effective method for generating multiple linear regression rules from artificial neural networks. In Proceedings of the 13th International Conference on Tools with Artificial Intelligence, Dallas, Texas, November 2001, pages 159-166.
  32. B. Baesens, R. Setiono, V. De Lille, S. Viaene and J. Vanthienen. Neural network rule extraction for credit scoring. In Proceedings of The Pacific Asian Conference on Intelligent Systems, PAIS'2001, Seoul, Korea, November 2001, pages 128-132.
  33. R. Setiono. Generating linear regression rules from neural networks using local least squares approximation. In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Proceedings of 6th International Work-Conference on Artificial Neural Networks, IWANN 2001, Granada, Spain, June 12 - 15, 2001, pages 277-284.
  34. R. Setiono, W.K. Leow and J.Y-L. Thong. Opening the neural network blackbox: An algorithm for extracting rules from function approximating neural networks. In Proceedings of ICIS 2000, International Conference on Information Systems, Brisbane, Australia, December 10 - 13, 2000.
  35. R. Setiono and W.K. Leow. Pruned neural networks for regression. In Proceedings of PRICAI 2000, 6th Pacific Rim International Conference on Artificial Intelligence, Melbourne, Australia, August 28 - September 1, 2000, pages 500-509.
  36. R. Setiono and A. Gaweda. Neural network pruning for function approximation. In Proceedings of International Joint Conference on Neural Networks, Como, Italy, July 2000.
  37. W.K. Leow, Z. Huang, Y. Zhang and R. Setiono. Rapid 3D model acquisition from images of small objects. In Proceedings of Geometric Modeling and Processing 2000, Theory and Applications, Hongkong, April 2000, pages 33-41.
  38. Y. Hayashi, R. Setiono and K. Yoshida. Diagnosis of hepatobiliary disorders using rules extracted from artificial neural networks. In Proceedings of 1999 IEEE International Fuzzy Systems Conference, Seoul, Korea, August 1999, pages I-344 - I-348.
  39. W.K. Leow and R. Setiono. Generating rules from trained network using fast pruning. In Proceedings of International Joint Conference on Neural Networks, Washington D.C. July 1999, pages 4095-4098.
  40. H. Liu and R. Setiono. Feature transformation and multivariate decision tree induction. In Proceedings of the 1st International Conference on Discovery Science, Fukuoka, Japan, December 1998.
  41. R. Setiono and H. Liu. Fragmentation problem and automated feature construction. In Proceedings of the 10th International Conference on Tools with Artificial Intelligence, Taipei, Taiwan, November 1998, pages 208-215.
  42. R. Setiono. Techniques for extracting rules from artificial neural networks. Plenary Lecture presented at the 5th International Conference on Soft Computing and Information Systems, Iizuka, Japan, October 1998.
  43. H. Liu and R. Setiono. Scalable feature selection for large sized databases. In Proceedings of 4th World Congress on Expert Systems, Mexico City, Mexico, March 1998, pages 521-528.
  44. R. Setiono. Generating piece-wise linear classifier using neural networks and its applications to bankruptcy prediction. In Proceedings of the 4th International Meeting Decision Sciences Institute, Sydney, Australia, July 1997, pages 762-764.
  45. R. Setiono and H. Liu. An analysis of internal representations by greedy clustering. ECML'97 - Poster Papers, Prague, Czech Republic, April 1997, pages 98-107.
  46. R. Setiono and H. Liu. NeuroLinear: A system for extracting oblique decision rules from neural networks. In Machine Learning: ECML-97, 9th European Conference on Machine Learning, Lecture Notes in Artificial Intelligence: Vol. 1224 Springer, Prague, Czech Republic, April 1997, pages 221-233.
  47. H. Liu and R. Setiono. A probabilistic approach to feature selection - a filter solution. In Machine Learning, Proc. of the 13th International Conference, Bari, Italy, July 1996, pages 319-327.
  48. H. Liu and R. Setiono. Feature selection and classification - a probabilistic wrapper approach. In Proc. 9th International Conference on Industrial & Engineering Applications of AI and Expert Systems, Fukuoka, Japan, June 1996, pages 419-424.
  49. H. Liu and R. Setiono. Chi2: Feature selection and discretization of numeric attributes. In Proceedings of the 7th International Conference on Tools with Artificial Intelligence, Washington D.C., November 1995, pages 388-391.
  50. H. Lu, R. Setiono, and H. Liu. NeuroRule: a connectionist approach to data mining. In Proceedings of 21st International Conference on Very Large Data Bases, Zurich, Switzerland, September 1995, pages 478-489.
  51. R. Setiono, and H. Liu. Understanding neural networks via rule extraction. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Canada, August 1995, pages 480-485.

Book chapters

  1. R. Setiono. Improving Credit Scoring Accuracy via Sample Selection. In The Best Thinking in Business Analytics. Decision Science Institute, M. Warkentin (Ed.), pages 67-78, Pearson Education Ltd, 2015.
  2. R. Setiono. Finding Relevant Variables and Interactions in Neural Network Credit Scoring Models. In Trends and Research in the Decision Sciences: Best Papers from the 2014 Annual Conference. Decision Science Institute, M. Warkentin (Ed.), pages 281-296, Pearson FT Press, 2014.
  3. R. Setiono, B. Baesens and D. Martens. Rule Extraction from Neural Networks and Support Vector Machines for Credit Scoring. In Data Mining: Foundations and Intelligent Paradigms. (D.E. Holmes, L.C. Jain (Eds.), pages 299-320, Springer-Verlag, 2011.
  4. D. Martens, J. Huysmans, R. Setiono, B. Baesens and J. Vanthienen. Rule Extraction from Support Vector Machines: An Overview of Issues and Application to Credit Scoring. In Rule Extraction from Support Vector Machines. (J. Diederich, Ed.), pages 33-59, Springer, 2008.
  5. A. Azcarraga, M.H. Hsieh, S.L. Pan and R. Setiono. Improved SOM Labeling Methodology for Data Mining Applications. In Soft Computing for Knowledge Discovery and Data Mining. (O. Maimon and L. Rokach, Eds.), Springer, pages 45-75, Springer, 2008.
  6. C. Mues, B. Baesens, R. Setiono and J. Vanthienen. From Knowledge Discovery to Implementation: A Business Intelligence Approach Using Neural Network Rule Extraction and Decision Tables. In Professional Knowledge Management: Third Biennial Conference, WM 2005, Kaiserlautern, Germany, April 10-13, 2005, Revised Selected Papers. (K. Althoff, A. Dengel, R. Bergmann, M. Nick and T. Roth-Berghofer, Eds.), pages 483 - 495, Springer-Verlag, GmbH, 2005.
  7. R. Setiono and J. Zurada. Knowledge Discovery from Continuous Data Using Artificial Neural Networks. In Machine Intelligence: Quo Vadis? (P. Sincak, J. Vascak and K. Hirota, Eds.), pages 217 - 232, World Scientific, 2004.
  8. B. Baesens, C. Mues, R. Setiono, M. De Backer and J. Vanthienen. Building Intelligent Credit Scoring Systems using Decision Tables. In Enterprise Information Systems V (O. Camp, J.B. Filipe, S. Hammoudi and M.G. Piattini, Eds.), Kluwer, 2003.
  9. R. Setiono and J.M. Zurada. A hybrid connectionist system for multiple regression. In Advances in Soft Computing Neural Networks and Soft Computing (L. Rutkowski and J. Kacprzyk, Eds.), pages 87 - 94, Physica-Verlag, 2002.
  10. R. Setiono. Techniques for Extracting Classification and Regression Rules from Artificial Neural Networks. In Computational Intelligence: The Experts Speak (D.B. Fogel and C. Robinson, Eds.), pages 99 - 113, IEEE and John Wiley & Sons, 2002.
  11. B. Baesens, R. Setiono, C. Mues, S. Viaene and J. Vanthienen. Building Intelligent Credit-Risk Evaluation Systems using Neural Network Rule Extraction and Decision Tables. In New Directions in Software Engineering (J. Vandenbuckle and M. Snoeck, Eds.), pages 121 - 133, Leuven University Press, 2001.
  12. R. Setiono, J. Y. L. Thong and C.S. Yap. Extracting Rules Concerning Market Segmentation from Artificial Neural Networks. In Business Applications of Neural Networks (P.J.G. Lisboa, A. Vellido and B. Edisburry, Eds.), pages 13 - 28, World Scientific Publishing Company, 2000.
  13. R. Setiono and H. Liu. Feature Extraction via Neural Networks. In Feature Extraction, Construction and Selection: A Data Mining Perspective (H. Motoda and H. Liu, Eds.), pages 191 - 204, Kluwer Academic Publishers, 1999.
  14. R. Setiono. Image Processing and Pattern Recognition: Algorithmic Techniques and Their Applications. In Neural Network Systems Techniques and Applications (C.T. Leondes, Ed.) Vol. 5, pages 287-319, Academic Press, 1998.
  15. O.L. Mangasarian, R. Setiono and W.H. Wolberg. Pattern Recognition via Linear Programming: Theory and Application to Medical Diagnosis. In Large-Scale Numerical Optimization (T.F. Coleman and Y. Li, Eds.) Chapter 2, pages 22-31, SIAM, 1989.
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