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Neuro-rough-fuzzy approach for regression modelling from missing data

International Journal of Applied Mathematics and Computer Science's Cover Image
International Journal of Applied Mathematics and Computer Science
Analysis and Control of Spatiotemporal Dynamic Systems (special section, pp. 245 - 326), Dariusz Uciński and Józef Korbicz (Eds.)

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Acuña, E. and Rodriguez, C. (2004). The treatment of missing values and its effect in the classifier accuracy, in D. Banks, L. House, F. McMorris, P. Arabie and W. Gaul (Eds.), Classification, Clustering and Data Mining Applications, Springer, Berlin/Heidelberg, pp. 639-648.10.1007/978-3-642-17103-1_60Search in Google Scholar

Box, G. E. P. and Jenkins, G. (1970). Time Series Analysis, Forecasting and Control, Holden-Day, Oakland, CA.Search in Google Scholar

Chan, L. S., Gilman, J. A. and Dunn, 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: 267-285.10.1016/S0167-6393(00)00034-0Search in Google Scholar

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

Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B 39(1): 1-38.10.1111/j.2517-6161.1977.tb01600.xSearch in Google Scholar

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

Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters, Journal Cybernetics 3(3): 32-57.10.1080/01969727308546046Search in Google Scholar

Farhangfar, A., Kurgan, L. and Dy, J. (2008). Impact of imputation of missing values on classification error for discrete data, Pattern Recognition 41(12): 3692-3705.10.1016/j.patcog.2008.05.019Search in Google Scholar

Farhangfar, A., Kurgan, L. and Pedrycz, W. (2007). A novel framework for imputation of missing values in databases, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 37(5): 692-709.10.1109/TSMCA.2007.902631Search in Google Scholar

Fuller, W. A. and Kim, J. K. (2005). Hot deck imputation for the response model, Survey Methodology 31(2): 139-149.Search in Google Scholar

Ghahramani, Z. and Jordan, M. (1995). Learning from incomplete data, Technical report, Lab Memo No. 1509, CBCL Paper No. 108, MIT AI Lab, Cambridge, MA.Search in Google Scholar

Grzymala-Busse, J. (2006). A rough set approach to data with missing attribute values, in G. Wang, J. Peters, A. Skowron and Y. Yao (Eds.), Rough Sets and Knowledge Technology, Lecture Notes in Computer Science, Vol. 4062, Springer, Berlin/Heidelberg, pp. 58-67.10.1007/11795131_10Search in Google Scholar

Grzymala-Busse, J. W. and Hu, M. (2001). A comparison of several approaches to missing attribute values in data mining, in W. Ziarko and Y. Yao (Eds.), Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science, Vol. 2005, Springer, Berlin/Heidelberg, pp. 378-385.10.1007/3-540-45554-X_46Search in Google Scholar

Hathaway, R. and Bezdek, J. (2001). Fuzzy c-means clustering of incomplete data, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 31(5): 735-744.10.1109/3477.956035Search in Google Scholar

Himmelspach, L. and Conrad, S. (2010). Fuzzy clustering of incomplete data based on cluster dispersion, in E. Hüllermeier, R. Kruse and F. Hoffmann (Eds.), Computational Intelligence for Knowledge-Based Systems Design, 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28-July 2, 2010. Proceedings, Lecture Notes in Computer Science, Vol. 6178, Springer, Berlin/Heidelberg, pp. 59-68.10.1007/978-3-642-14049-5_7Search in Google Scholar

Hwang, C. and Rhee, F. C.-H. (2004). An interval type-2 fuzzy c spherical shells algorithm, Proceedings of the 2004 IEEE International Conference on Fuzzy Systems, Budapest, Hungary, pp. 1117-1122.Search in Google Scholar

Korytkowski, M., Nowicki, R., Scherer, R. and Rutkowski, L. (2008). Ensemble of rough-neuro-fuzzy systems for classification with missing features, IEEE International Conference on Fuzzy Systems, FUZZ-IEEE (IEEE World Congress on Computational Intelligence), Hong Kong, China, pp. 1745-1750.Search in Google Scholar

Lakshminarayan, K., Harp, S. A. and Samad, T. (1999). Imputation of missing data in industrial databases, Applied Intelligence 11(3): 259-275, DOI: 10.1023/A:1008334909089.10.1023/A:1008334909089Search in Google Scholar

Łęski, J. (2008). Neuro-Fuzzy Systems, Wydawnictwa Naukowo-Techniczne, Warsaw, (in Polish).Search in Google Scholar

Łęski, J. and Czogała, E. (1999). A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications, Fuzzy Sets and Systems 108(3): 289-297.10.1016/S0165-0114(97)00314-XSearch in Google Scholar

Mamdani, E. H. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies 7(1): 1-13.10.1016/S0020-7373(75)80002-2Search in Google Scholar

Nowicki, R. (2006). Rough-neuro-fuzzy system with MICOG defuzzification, 2006 IEEE International Conference on Fuzzy Systems, Vancouver, Canada, pp. 1958-1965.Search 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 and Data Engineering 20(9): 1239-1253.10.1109/TKDE.2008.64Search in Google Scholar

Nowicki, R. K. (2009). Rough-neuro-fuzzy structures for classification with missing data, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 39(6): 1334-1347.10.1109/TSMCB.2009.201250419366645Search in Google Scholar

Nowicki, R. K. (2010). On classification with missing data using rough-neuro-fuzzy systems, International Journal of Applied Mathematics and Computer Science 20(1): 55-67, DOI: 10.2478/v10006-010-0004-8.10.2478/v10006-010-0004-8Search in Google Scholar

Pawlak, Z. (1982). Rough sets, International Journal of Parallel Programming 11(5): 341-356.10.1007/BF01001956Search in Google Scholar

Pedrycz, W. (1998). Conditional fuzzy clustering in the design of radial basis function neural networks, IEEE Transactions on Neural Networks 9(4): 601-612.10.1109/72.701174Search 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

Rubin, D. (1987). Multiple Imputation for Nonresponse in Surveys, John Wiley & Sons, New York, NY.10.1002/9780470316696Search in Google Scholar

Sugeno, M. and Kang, G. T. (1988). Structure identification of fuzzy model, Fuzzy Sets and Systems 28(1): 15-33.10.1016/0165-0114(88)90113-3Search in Google Scholar

Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on Systems, Man and Cybernetics 15(1): 116-132.10.1109/TSMC.1985.6313399Search in Google Scholar

Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D. and Altman, R. B. (2001). Missing value estimation methods for DNA microarrays, Bioinformatics 17(6): 520-525.10.1093/bioinformatics/17.6.520Search in Google Scholar

Wagstaff, K. (2004). Clustering with missing values: No imputation required, in D. Banks, L. House, F. R. Mcmorris, P. Arabie and W. Gaul (Eds.), Classification, Clustering, and Data Mining Applications (Proceedings of the Meeting of the International Federation of Classification Societies), Springer, Berlin/Heidelberg, pp. 649-658.10.1007/978-3-642-17103-1_61Search in Google Scholar

Wagstaff, K. L. and Laidler, V. G. (2005). Making the most of missing values: Object clustering with partial data in astronomy, Proceedings of Astronomical Data Analysis Software and Systems XIV, Pasadena, CA, Vol. 347, pp. 172-176.Search in Google Scholar

Yeh, I. C. (1998). Modeling of strength of high-performance concrete using artificial neural networks, Cement and Concrete Research 28(12): 1797-1808.10.1016/S0008-8846(98)00165-3Search in Google Scholar

Zhang, C., Zhu, X., Zhang, J., Qin, Y. and Zhang, S. (2007). GBKII: An imputation method for missing values, Advances in Knowledge Discovery and Data Mining 4426: 1080-1087.10.1007/978-3-540-71701-0_122Search in Google Scholar

Zhang, S. (2011). Shell-neighbor method and its application in missing data imputation, Applied Intelligence 35(1): 1-11, DOI: 10.1007/s10489-009-0207-6.10.1007/s10489-009-0207-6Search in Google Scholar

ISSN:
1641-876X
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Mathematics, Applied Mathematics