On classification with missing data using rough-neuro-fuzzy systems

Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Clarendon Press, Oxford.10.1201/9781420050646.ptb6Search in Google Scholar

Broekhoven, E. V. and Beats, B. D. (2006). Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions, Fuzzy Sets and Systems 157(7): 904-918.10.1016/j.fss.2005.11.005Search in Google Scholar

Chan, L. S., Gilman, J. A. and Dun, O. J. (1976). Alternative approaches to missing values in discriminant analysis, Journal of the American Statistical Association 71(356): 842-844.10.1080/01621459.1976.10480956Search in Google Scholar

Cooke, M., Green, P., Josifovski, L. and Vizinho, A. (2001). Robust automatic speech recognition with missing and unreliable acoustic data, Speech Communication 34(3): 267-285.10.1016/S0167-6393(00)00034-0Search in Google Scholar

Czogała, E. and Łęski, J. (2000). Fuzzy and Neuro-Fuzzy Intelligent Systems, Physica-Verlag, Heidelberg/New York, NY.10.1007/978-3-7908-1853-6Search in Google Scholar

Dixon, J. K. (1979). Pattern recognition with partly missing data, IEEE Transactional on Systems, Man and Cybernetics 9(10): 617-621.10.1109/TSMC.1979.4310090Search in Google Scholar

Driankov, D., Hellendoorn, H. and Reinfrank, M. (1993). An Introduction to Fuzzy Control, Springer-Verlag, Berlin/Heidelberg.10.1007/978-3-662-11131-4Search in Google Scholar

Dubois, D. and Prade, H. (1990). Rough fuzzy sets and fuzzy rough sets, International Journal of General Systems 17(2-3): 191-209.10.1080/03081079008935107Search in Google Scholar

Dubois, D. and Prade, H. (1992). Putting rough sets and fuzzy sets together, in R. Słowiński (Ed.), Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer, Dordrecht, pp. 203-232.10.1007/978-94-015-7975-9_14Search in Google Scholar

Duda, R. O., Hart, P. E. and Stork, D. G. (2001). Pattern Classification, A Wiley-Interscience Publication, John Wiley & Sons, Inc., New York, NY/Chichester/Weinheim/Brisbane/Singapore/Toronto.Search in Google Scholar

Fodor, J. C. (1991). On fuzzy implication operators, Fuzzy Sets and Systems 42(3): 293-300.10.1016/0165-0114(91)90108-3Search in Google Scholar

Fogel, D. B. (1995). Evolutionary Computation: Towards a New Philosophy of Machine Intelligence, IEEE Press, New York, NY.Search in Google Scholar

Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company Inc, Reading, MA.Search in Google Scholar

Grzymala-Busse, J.W. (1989). An overview of the LERS1 learning systems, Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Tullahoma, TN, USA, pp. 838-844.Search in Google Scholar

Grzymala-Busse, J. W. (1992). LERS—A system for learning from examples based on rough sets, in R. Slowinski (Ed.), Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer, Dordrecht, pp. 3-18.10.1007/978-94-015-7975-9_1Search in Google Scholar

Jin, Y. (2000). Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement, IEEE Transactions on Fuzzy Systems 8(2): 212-221.10.1109/91.842154Search in Google Scholar

Kecman, V. (2001). Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA.Search in Google Scholar

Klement, E. P., Mesiar, R. and Pap, E. (2000). Triangular Norms, Kluwer Academic Publishers, Dordrecht.10.1007/978-94-015-9540-7Search in Google Scholar

Kuncheva, L. I. (2000). Fuzzy Classifier Design. Studies in Fuzziness and Soft Computing, Physica-Verlag, Heidelberg.10.1007/978-3-7908-1850-5_5Search in Google Scholar

Lee, K. M. and Kwang, D. H. (1994). A fuzzy neural network model for fuzzy inference and rule tuning, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2(3): 265-277.10.1142/S0218488594000213Search in Google Scholar

Lin, C. T. and Lee, G. C. S. (1991). Neural-network-based fuzzy logic control and decision system, IEEE Transactions on Computers 40(12): 1320-1336.10.1109/12.106218Search in Google Scholar

Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd Edn, Wiley-Interscience, New York, NY.10.1002/9781119013563Search in Google Scholar

Marin, N., Molina, C., Serrano, J. and Vila, M. (2008). A complexity guided algorithm for association rule extraction on fuzzy datacubes, IEEE Transactions on Fuzzy Systems 16(3): 693-714.10.1109/TFUZZ.2007.905909Search in Google Scholar

Mas, M., Monserrat, M., Torrens, J., and Trillas, E. (2007). A survey on fuzzy implication functions, IEEE Transactions on Fuzzy Systems 15(6): 1107-1121.10.1109/TFUZZ.2007.896304Search in Google Scholar

Mertz, C. J. and Murphy, P. M. (n.d.). UCI respository of machine learning databases http://www.ics.uci.edu/pub/machine-learning-databasesSearch in Google Scholar

Michalewicz, Z. (1992). Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin/Heidelberg/New York, NY.10.1007/978-3-662-02830-8Search in Google Scholar

Morin, R. L. and Raeside, D. E. (1981). A reappraisal of distance-weighted k-nearest neighbor classification for pattern recognition with missing data, IEEE Transactions on Systems, Man and Cybernetics 11(3): 241-243.10.1109/TSMC.1981.4308660Search in Google Scholar

Nauck, D., Klawonn, F. and Kruse, R. (1997). Foundations of Neuro-Fuzzy Systems, Wiley, Chichester.Search in Google Scholar

Nowicki, R. (2000). The Neuro-Fuzzy Systems which Realizes Various Methods of Fuzzy Reasoning, Ph.D. thesis, AGH University of Science and Technology, Cracow, (in Polish).Search in Google Scholar

Nowicki, R. (2004). Rough sets in the neuro-fuzzy architectures based on non-monotonic fuzzy implications, in L. Rutkowski, J. Siekmann, R. Tadeusiewicz and L.A. Zadeh (Eds), Artificial Inteligence and Soft Computing—ICAISC 2004, Lecture Notes in Artificial Intelligence, Springer-Verlag, Berlin/Heidelberg, Vol. 3070, pp. 518-525.10.1007/978-3-540-24844-6_77Search in Google Scholar

Nowicki, R. (2008). On combining neuro-fuzzy architectures with the rough set theory to solve classification problems with incomplete data, IEEE Transactions on Knowledge Data Engineering 20(9): 1239-1253.10.1109/TKDE.2008.64Search in Google Scholar

Nowicki, R. (2009). Rough-neuro-fuzzy structures for classification with missing data, IEEE Transactions on Systems, Man and Cybernetics B 39(6): 1334-1347.10.1109/TSMCB.2009.2012504Search in Google Scholar

Nowicki, R. and Rutkowska, D. (2000). Neuro-fuzzy architectures based on yager implication, Proceedings of the 5th Conference on Neural Networks and Soft Computing, Zakopane, Poland, pp. 353-360.Search in Google Scholar

Patel, A. V. and Mohan, B. M. (2002). Some numerical aspects of center of area defuzzification method, Fuzzy Sets and Systems 132(3): 401-409.10.1016/S0165-0114(02)00107-0Search in Google Scholar

Pawlak, Z. (1982). Rough sets, International Journal of Information and Computer Science 11(341): 341-356.10.1007/BF01001956Search in Google Scholar

Pawlak, Z. (1991). Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer, Dordrecht.10.1007/978-94-011-3534-4Search in Google Scholar

Pawlak, Z. (2002). Rough sets, decision algorithms and Bayes' theorem, European Journal of Operational Research 136(1): 181-189.10.1016/S0377-2217(01)00029-7Search in Google Scholar

Pedrycz, W. and Bargiela, A. (2002). Granular clustering: A granular signature of data, IEEE Transactions on Systems, Man and Cybernetics B 32(2): 212-224.10.1109/3477.99087818238121Search in Google Scholar

Polkowski, L. (2002). Rough Sets. Mathematical Foundation, Physica-Verlag, Heidelberg/New York, NY.10.1007/978-3-7908-1776-8Search in Google Scholar

Renz, C., Rajapakse, J. C., Razvi, K. and Liang, S. K. C. (2002). Ovarian cancer classification with missing data, Proceedings of the 9th International Conference on Neural Information Processing, ICONIP'02, Singapore, Vol. 2, pp. 809-813.Search in Google Scholar

Rutkowska, D. and Nowicki, R. (2000a). Implication-based neuro-fuzzy architectures, International Journal of Applied Mathematics and Computer Science 10(4): 675-701.Search in Google Scholar

Rutkowska, D. and Nowicki, R. (2000b). New neuro-fuzzy architectures, Proceedings of the International Conference on Artificial and Computational Intelligence for Decision, Control and Automation in Engineering and Industrial Applications, AcIDcA'2000, Monastir, Tunisia, pp. 82-87.Search in Google Scholar

Rutkowska, D., Nowicki, R. and Rutkowski, L. (2000). Neuro-fuzzy architectures with various implication operators, The State of the Art in Computational Intelligence—Proceedings of the International Symposium on Computational Intelligence (ISCI 2000), Kosice, Slovakia, pp. 214-219.Search in Google Scholar

Rutkowski, L. and Cpałka, K. (2003). Flexible neuro-fuzzy systems, IEEE Transactions on Neural Networks 14(3): 554-574.10.1109/TNN.2003.811698Search in Google Scholar

Rutkowski, L. and Cpałka, K. (2005). Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems, IEEE Transactions on Fuzzy Systems 13(1): 140-151.10.1109/TFUZZ.2004.836069Search in Google Scholar

Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery 2(2): 121-167.10.1023/A:1009715923555Search in Google Scholar

Tanaka, M., Kotokawa, Y. and Tanino, T. (1996). Patern classification by stochastic neural network with missing data, IEEE International Conference on System, Man and Cybernetics, Beijing, China, Vol. 1, pp. 690-695.Search in Google Scholar

Wang, L. X. (1994). Adaptive Fuzzy Systems and Control, PTR Prentice Hall, Englewood Cliffs, NJ.Search in Google Scholar

Yager, R. R. and Filev, D. P. (1994). Essentials of Fuzzy Modeling and Control, John Wiley and Sons, New York, NY.Search in Google Scholar

Yao, J. T. and Yao, Y. Y. (2002). Induction of classification rules by granular computing, in J.J. Alpigini, J.F. Peters, J.F. Skowron and N. Zhang (Eds), Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence, Springer, Berlin/Heidelberg, Vol. 2475, pp. 331-338.10.1007/3-540-45813-1_43Search in Google Scholar

Zadeh, L. A. (1965). Fuzzy sets, Information and Control 8(3): 338-353.10.1016/S0019-9958(65)90241-XSearch in Google Scholar

Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—Part 1, Information Sciences 8(3): 199-249.10.1016/0020-0255(75)90036-5Search in Google Scholar

Żurada, J. M. (1992). Introduction to Artificial Neural Systems, West Publishing Company, St. Paul, MN.Search in Google Scholar