How to cite this paper
Rezvan, M., Hamadani, A., Saffari, B & Shalbafzadeh, A. (2014). A combined data mining approach using rough set theory and case-based reasoning in medical datasets.Decision Science Letters , 3(3), 285-294.
Refrences
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Chen, Y., Miao, D., & Wang, R. (2010). A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Letter, 31, 226–233.
Chuang, Ch.-L. (2013). Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction. Information Sciences, 236(1), 174-185.
Du, Y., Liang, F., & Sun Y. (2012). Integrating spatial relations into case-based reasoning to solve geographic problems. Knowledge-Based Systems, 33, 111-123.
De Stefano, C., Folino, G., Fontanella, F., & Scotto di Freca A. (2014). Using Bayesian networks for selecting classifiers in GP ensembles. Information Sciences, 258, 200-216.
Hedar, Ab-R., Wang, J., & Fukushima, M. (2008). Tabu search for attribute reduction in rough set theory. Soft Computing, 12, 909-918.
Hettich, S., Blake, C. L., Merz, C. J., UCI repository of data mining databases, 1998. Available from: http://www.ics.uci.edu// mlearn /MLRepository.html.
Huang, Ch.-Ch., & Tseng, T.-L. (2004). Rough set approach to case-based reasoning application. Expert Systems with Applications, 26, 369–385.
Jiang, Y-J., Chen J., & Ruan X.-Y. (2006). Fuzzy similarity-based rough set method for case-based reasoning and its application in tool selection. International Journal of Machine Tools & Manufacture, 46, 107–113.
Khashei, M., Rezvan, M.T., Zeinal Hamadani, A., & Bijari, M. (2013). Bi-level neural-based fuzzy classification approach for credit scoring problems. Complexity, 18(6), 46-57.
Li, Y., Shiu, S.C.K., Pal, S.K., & Liu, J.N.K. (2006). A rough set-based case-based reasoner for text categorization. International Journal of Approximate Reasoning, 41(2), 229-255.
Lin, R.-H., Wang, Y.-T., Wu, Ch.-H., & Chuang Ch.-L. (2009). Developing a business failure prediction model via RST, GRA and CBR. Expert Systems with Applications, 36 (2), 1593-1600.
Liu, G., & Yu, W. (2009). Smart case-based indexing in worsted roving process: Combination of rough set and case-based reasoning. Applied Mathematics and Computation, 214, 280–286.
Lu, X., Huang, Zh., & Duan, H. (2012). Supporting adaptive clinical treatment processes through recommendations. Computer Methods and Programs in Biomedicine, 107 (3), 413-424.
Louhi-Kultanen, M., Kraslawski, A., & Avramenko Y. (2009). Case-based reasoning for crystallizer selection using rough sets and fuzzy sets analysis. Chemical Engineering and Processing, 48, 1193–1198.
Melville, P., & Mooney, R.J. (2005). Creating diversity in ensembles using artificial data. Information Fusion, 6, 99–111.
Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Science, 11, 341–356.
Pawlak, Z. (1991). Rough Sets-Theoretical Aspects of Reasoning about Data. Kluwer Academic Publisher, Netherlands.
Quinlan, J.R. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4, 77–90.
Rao, D.V., & Sarma, V.V.S. (2003). A rough-fuzzy approach for retrieval of candidate components for software reuse. Pattern Recognition Letters, 24 (6), 875-886.
Rezvan, M.T., Zeinal Hamadani, A., & Shalbafzadeh A. (2013). Case-based reasoning for classification in the mixed data sets employing the compound distance methods. Engineering Applications of Artificial Intelligence, 6, 2001-2009.
Rezvan, M.T., Zeinal Hamadani, A., & Hejazi S.R. (2014). An exact feature selection algorithm based on rough set theory. Complexity, March 2013, Available online, DOI: 10.1002/cplx.21526.
Reboiro-Jato, M., D?az, F. Glez-Pe?a, D., & Fdez-Riverola F. (2014). A novel ensemble of classifiers that use biological relevant gene sets for microarray classification, Applied Soft Computing, 17, 117-126.
Salam?, M., & L?pez-S?nchez, M. (2011). Rough set based approaches to feature selection for Case-Based Reasoning classifiers. Pattern Recognition Letters, 32, 280–292.
Schank, R. (1982). Dynamic memory: a theory of reminding and learning in computers and people. Cambridge University Press, Cambridge, UK.
Segal, R., & Etzioni, O. (1994). Learning decision lists using homogenous rules. American association artificial intelligence.
Ster, B., & Dobnikar, A. (1996). Neural networks in medical diagnosis: comparison with other methods, in: Proceedings of the International Conference on Engineering Applications of Neural Networks (EANN’96), 427–430.
Subashini, T.S., Ramalingam, V., & Palanivel, S. (2009). Breast mass classification based on cytological patterns using RBFNN and SVM. Expert Systems with Applications, 36, 5284–5290.
Wang, M., Yu, G., Xu, J., He, H., Yu, D., & An, Sh. (2012). Development a case-based classifier for predicting highly cited papers. Journal of Informetrics, 6 (4), 586-599.
Watson, I. (1999). Case-based reasoning is a methodology not a technology. Knowledge-Based Systems, 12 (5/6), 303–308.
Yao, Y., Zhao, Y., & Wang, J. (2008). On reduct construction algorithms. Transaction computer Science, 100–117.
Yao, Y., & Zhao, Y. (2009). Discernibility matrix simplification for constructing attribute reducts. Information Science, 179 (7), 867–882.
Yun, J., Zhanhuai, L., Yang, Z., & Qiang, Z. (2004). A new approach for selecting attributes based on rough set theory. Intelligent Data Engineering and Automated Learning, 152–158.
Zeinal Hamadani, A., Shalbafzadeh A. Rezvan, T., & Shahlayi Moghadam A. (2013). An integrated genetic-based model of naïve bayes networks for credit scoring. International Journal of Artificial Intelligence & Applications (IJAIA), 4(1), 85-103.
Zhang, M., & Yao, J. (2004). A rough sets based approach to feature selection. In: Proceedings 23rd Internet Conference of NAFIPS, 434–439.
Chen, Y., Miao, D., & Wang, R. (2010). A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Letter, 31, 226–233.
Chuang, Ch.-L. (2013). Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction. Information Sciences, 236(1), 174-185.
Du, Y., Liang, F., & Sun Y. (2012). Integrating spatial relations into case-based reasoning to solve geographic problems. Knowledge-Based Systems, 33, 111-123.
De Stefano, C., Folino, G., Fontanella, F., & Scotto di Freca A. (2014). Using Bayesian networks for selecting classifiers in GP ensembles. Information Sciences, 258, 200-216.
Hedar, Ab-R., Wang, J., & Fukushima, M. (2008). Tabu search for attribute reduction in rough set theory. Soft Computing, 12, 909-918.
Hettich, S., Blake, C. L., Merz, C. J., UCI repository of data mining databases, 1998. Available from: http://www.ics.uci.edu// mlearn /MLRepository.html.
Huang, Ch.-Ch., & Tseng, T.-L. (2004). Rough set approach to case-based reasoning application. Expert Systems with Applications, 26, 369–385.
Jiang, Y-J., Chen J., & Ruan X.-Y. (2006). Fuzzy similarity-based rough set method for case-based reasoning and its application in tool selection. International Journal of Machine Tools & Manufacture, 46, 107–113.
Khashei, M., Rezvan, M.T., Zeinal Hamadani, A., & Bijari, M. (2013). Bi-level neural-based fuzzy classification approach for credit scoring problems. Complexity, 18(6), 46-57.
Li, Y., Shiu, S.C.K., Pal, S.K., & Liu, J.N.K. (2006). A rough set-based case-based reasoner for text categorization. International Journal of Approximate Reasoning, 41(2), 229-255.
Lin, R.-H., Wang, Y.-T., Wu, Ch.-H., & Chuang Ch.-L. (2009). Developing a business failure prediction model via RST, GRA and CBR. Expert Systems with Applications, 36 (2), 1593-1600.
Liu, G., & Yu, W. (2009). Smart case-based indexing in worsted roving process: Combination of rough set and case-based reasoning. Applied Mathematics and Computation, 214, 280–286.
Lu, X., Huang, Zh., & Duan, H. (2012). Supporting adaptive clinical treatment processes through recommendations. Computer Methods and Programs in Biomedicine, 107 (3), 413-424.
Louhi-Kultanen, M., Kraslawski, A., & Avramenko Y. (2009). Case-based reasoning for crystallizer selection using rough sets and fuzzy sets analysis. Chemical Engineering and Processing, 48, 1193–1198.
Melville, P., & Mooney, R.J. (2005). Creating diversity in ensembles using artificial data. Information Fusion, 6, 99–111.
Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Science, 11, 341–356.
Pawlak, Z. (1991). Rough Sets-Theoretical Aspects of Reasoning about Data. Kluwer Academic Publisher, Netherlands.
Quinlan, J.R. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4, 77–90.
Rao, D.V., & Sarma, V.V.S. (2003). A rough-fuzzy approach for retrieval of candidate components for software reuse. Pattern Recognition Letters, 24 (6), 875-886.
Rezvan, M.T., Zeinal Hamadani, A., & Shalbafzadeh A. (2013). Case-based reasoning for classification in the mixed data sets employing the compound distance methods. Engineering Applications of Artificial Intelligence, 6, 2001-2009.
Rezvan, M.T., Zeinal Hamadani, A., & Hejazi S.R. (2014). An exact feature selection algorithm based on rough set theory. Complexity, March 2013, Available online, DOI: 10.1002/cplx.21526.
Reboiro-Jato, M., D?az, F. Glez-Pe?a, D., & Fdez-Riverola F. (2014). A novel ensemble of classifiers that use biological relevant gene sets for microarray classification, Applied Soft Computing, 17, 117-126.
Salam?, M., & L?pez-S?nchez, M. (2011). Rough set based approaches to feature selection for Case-Based Reasoning classifiers. Pattern Recognition Letters, 32, 280–292.
Schank, R. (1982). Dynamic memory: a theory of reminding and learning in computers and people. Cambridge University Press, Cambridge, UK.
Segal, R., & Etzioni, O. (1994). Learning decision lists using homogenous rules. American association artificial intelligence.
Ster, B., & Dobnikar, A. (1996). Neural networks in medical diagnosis: comparison with other methods, in: Proceedings of the International Conference on Engineering Applications of Neural Networks (EANN’96), 427–430.
Subashini, T.S., Ramalingam, V., & Palanivel, S. (2009). Breast mass classification based on cytological patterns using RBFNN and SVM. Expert Systems with Applications, 36, 5284–5290.
Wang, M., Yu, G., Xu, J., He, H., Yu, D., & An, Sh. (2012). Development a case-based classifier for predicting highly cited papers. Journal of Informetrics, 6 (4), 586-599.
Watson, I. (1999). Case-based reasoning is a methodology not a technology. Knowledge-Based Systems, 12 (5/6), 303–308.
Yao, Y., Zhao, Y., & Wang, J. (2008). On reduct construction algorithms. Transaction computer Science, 100–117.
Yao, Y., & Zhao, Y. (2009). Discernibility matrix simplification for constructing attribute reducts. Information Science, 179 (7), 867–882.
Yun, J., Zhanhuai, L., Yang, Z., & Qiang, Z. (2004). A new approach for selecting attributes based on rough set theory. Intelligent Data Engineering and Automated Learning, 152–158.
Zeinal Hamadani, A., Shalbafzadeh A. Rezvan, T., & Shahlayi Moghadam A. (2013). An integrated genetic-based model of naïve bayes networks for credit scoring. International Journal of Artificial Intelligence & Applications (IJAIA), 4(1), 85-103.
Zhang, M., & Yao, J. (2004). A rough sets based approach to feature selection. In: Proceedings 23rd Internet Conference of NAFIPS, 434–439.