References
- [1] Amiri, M., Jensen, R., Eftekhari, M., Parthaláin, N.M.: Dataset condensation using owa fuzzy-rough set-based nearest neighbor classifier. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1934–1941 (2016). DOI 10.1109/FUZZ-IEEE.2016.7737928
- [2] Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13 (1), 21–27 (1967)
- [3] Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems 17 (2–3), 191–209 (1990)
- [4] Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: R. Słowiński (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 203–232. Kluwer, Dordrecht (1992)
- [5] Dziwiński, P., Starczewski, J.T., Bartczuk, Ł.: New linguistic hedges in construction of interval type-2 fls. In: L. Rutkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, J.M. Zurada (eds.) Artifical Intelligence and Soft Computing, pp. 445–450. Springer Berlin Heidelberg, Berlin, Heidelberg (2010)
- [6] Grzymala-Busse, J.W.: An overview of the LERS1 learning systems. In: Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 838–844 (1989)
- [7] Grzymala-Busse, J.W.: LERS — a system for learning from examples based on rough sets. In: R. Słowiński (ed.) Intelligent Decision Support: Handbook of Applications and Advences of the Rough Sets Theory, pp. 3–18. Kluwer, Dordrecht (1992)
- [8] Guo, Q., Qu, Y., Deng, A.: Invasive weed optimisation inspired fuzzy-rough feature selection. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1942–1947 (2016). DOI 10.1109/FUZZ-IEEE.2016.7737929
- [9] Korytkowski, M., Nowicki, R., Scherer, R., Rutkowski, L.: Ensemble of rough–neuro–fuzzy systems for classification with missing features. In: Proceedings of World Congress on Computational Intelligence 2008, pp. 1745–1750 (2008)
- [10] Korytkowski, M., Nowicki, R.K., Rutkowski, L., Scherer, R.: MICOG defuzzification rough–neuro– fuzzy system ensemble. In: 2010 IEEE International Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence, pp. 2268–2273. Barcelona, Spain (2010)
- [11] Li, D., Zhang, H., Li, T., Bouras, A., Yu, X., Wang, T.: Hybrid missing value imputation algorithms using fuzzy c-means and vaguely quantified rough set. IEEE Transactions on Fuzzy Systems pp. 1–1 (2021). DOI 10.1109/TFUZZ.2021.3058643
- [12] Lingras, P.: Comparison of neofuzzy and rough neural networks. Information Sciences 110 (3–4), 207–215 (1998)
- [13] Lingras, P.: Fuzzy–rough and rough–fuzzy serial combinations in neurocomputing. Neurocomput. 36 (1–4), 29–44 (2001)
- [14] Liu, H., Tuo, H., Liu, Y.: Rough neural network of variable precision. Neural Processing Letters 19 (1), 73–87 (2004). DOI 10.1023/B:NEPL.0000016851.47914.40. URL https://doi.org/10.1023/B:NEPL.0000016851.47914.40
- [15] Mertz, C.J., Murphy, P.M.: UCI resposi-tory of machine learning databases. Available online: http://www.ics.uci.edu/pub/machine-learning-databases
- [16] Mitra, S., Hayashi, Y.: Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Transactions on Neural Networks 11 (3), 748–768 (2000). DOI 10.1109/72.846746
- [17] Nguyen, H.S.: On Exploring Soft Discretization of Continuous Attributes, pp. 333–350. Springer Berlin Heidelberg, Berlin, Heidelberg (2004)
- [18] Nowicki, R.: On combining neuro–fuzzy architectures with the rough set theory to solve classification problems with incomplete data. IEEE Trans. on Knowledge and Data Engineering 20 (9), 1239–1253 (2008). DOI 10.1109/TKDE.2008.64
- [19] Nowicki, R.: Rough–neuro–fuzzy structures for classification with missing data. IEEE Trans. on Systems, Man, and Cybernetics—Part B: Cybernetics 39 (6), 1334–1347 (2009). DOI 10.1109/TSMCB.2009.2012504
- [20] Nowicki, R.: On classification with missing data using rough-neuro-fuzzy systems. International Journal of Applied Mathematics and Computer Science 20 (1), 55–67 (2010)
- [21] Nowicki, R.K.: Rough Set–Based Classification Systems. Springer International Publishing, Cham (2019). DOI 10.1007/978-3-030-03895-3
- [22] Nowicki, R.K., Grzanek, K., Hayashi, Y.: Rough support vector machine for classification with interval and incomplete data. Journal of Artificial Intelligence and Soft Computing Research 10 (1), 47–56 (2020). DOI 10.2478/jaiscr-2020-0004
- [23] Nowicki, R.K., Korytkowski, M., Scherer, R.: Rough neural network ensemble for interval data classification. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7 (2018). DOI 10.1109/FUZZ-IEEE.2018.8491609
- [24] Pawlak, Z.: Information systems — theoretical foundations. Information Systems 6, 205–218 (1981)
- [25] Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11 (5), 341–356 (1982)
- [26] Qiu, W., Hu, Z.: Composed fuzzy rough set and its applications in fuzzy rsar. In: M. Xu, Y. Zhan, J. Cao, Y. Liu (eds.) Advanced Parallel Processing Technologies, pp. 753–763. Springer Berlin Heidelberg, Berlin, Heidelberg (2007)
- [27] Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
- [28] Starczewski, J.T.: Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty, Studies in Fuzziness and Soft Computing, vol. 284. Springer-Verlag, Berlin Heidelberg (2013)
- [29] Tsang, E.C.C., Zhao, S.: A fast algorithm to building a fuzzy rough classifier. In: X. Wang, W. Pedrycz, P. Chan, Q. He (eds.) Machine Learning and Cybernetics, pp. 409–417. Springer Berlin Heidelberg, Berlin, Heidelberg (2014)
- [30] Vapnik, V.: Estimation of Dependences Based on Empirical Data. Springer-Verlag (1982)
- [31] Żurada, J.M.: Introduction to Artificial Neural Systems. West Publishing Company (1992)