Performance analysis of rough set–based hybrid classification systems in the case of missing values

• Autorzy: Nowicki Robert K. , Seliga Robert , Żelasko Dariusz , Hayashi Yoichi

Identyfikatory

DOI

10.2478/jaiscr-2021-0018

• Języki publikacji: EN

Abstrakty

EN

The paper presents a performance analysis of a selected few rough set–based classification systems. They are hybrid solutions designed to process information with missing values. Rough set-–based classification systems combine various classification methods, such as support vector machines, k–nearest neighbour, fuzzy systems, and neural networks with the rough set theory. When all input values take the form of real numbers, and they are available, the structure of the classifier returns to a non–rough set version. The performance of the four systems has been analysed based on the classification results obtained for benchmark databases downloaded from the machine learning repository of the University of California at Irvine.

Słowa kluczowe

EN

rough sets; support vector machine; fuzzy system; neural networks

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2021
• Tom: Vol. 11, No. 4
• Strony: 307--318
• Opis fizyczny: Bibliogr. 31 poz., rys.

Twórcy

autor

Nowicki Robert K.

• Department of Intelligent Computer Systems, Czestochowa University of Technology, Czestochowa, Poland

autor

Seliga Robert

• Management Department, University of Social Science, 90–113 Lodz, Poland
• Clark University, Worcester, MA 01610, USA

autor

Żelasko Dariusz

• Faculty of Computer Science and Telecommunications, Cracow University of Technology Warszawska 24, 31-155 Krakow, Poland

autor

Hayashi Yoichi

• Department of Computer Science, Meiji University, Kawasaki 214-8571 Japan

Bibliografia

• [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)

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).