Abstract
This paper presents the recognition algorithm with random selection of features. In the proposed procedure of classification the choice of weights is one of the main problems. In this paper we propose the weighted majority vote rule in which weights are represented by interval-valued fuzzy set (IVFS). In our approach the weights have a lower and upper membership function. The described algorithm was tested on one data set from UCI repository. The obtained results are compared with the most popular majority vote and the weighted majority vote rule.
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References
Krupka, E., Navot, A., Tishby, N.: Learning to Select Features using their Properties. Journal of Machine Learning Research 9, 2349–2376 (2008)
Zadeh, L.A.: Probability measures of fuzzy events. Journal of Mathematical Analysis and Applications 23, 421–427 (1968)
Goguen, J.: L-fuzzy sets. Journal of Mathematical Analysis and Applications 18(1), 145–174 (1967)
Pawlak, Z.: Rough sets and fuzzy sets. Fuzzy Sets and Systems 17, 99–102 (1985)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning - I. Information Science 8, 199–249 (1975)
Burduk, R.: Imprecise information in Bayes classifier. Pattern Analysis and Applications 15(2), 147–153 (2012)
Mitchell, H.B.: Pattern recognition using type-II fuzzy sets. Information Science 170, 409–418 (2005)
Zeng, J., Liu, Y.-Q.: Type-2 fuzzy markov random fields and their application to handwritten chinese character recognition. IEEE Transactions on Fuzzy Systems 16(3), 747–760 (2008)
Melin, P.: Image Processing and Pattern Recognition with Mamdani Interval Type-2 Fuzzy Inference Systems. In: Trillas, E., Bonissone, P.P., Magdalena, L., Kacprzyk, J. (eds.) Combining Experimentation and Theory. STUDFUZZ, vol. 271, pp. 179–190. Springer, Heidelberg (2011)
Ho, T.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Kuncheva, L.I., Whitaker, C.J., Duin, R.P.W.: Limits on the majority vote accuracy in classifier fusion. Pattern Analysis and Applications 6, 22–31 (2003)
Zmyslony, M., Wozniak, M., Jackowski, K.: Comparative analysis of classifier fusers. International Journal of Artificial Intelligence & Applications 3(3), 95–109 (2012)
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Burduk, R. (2012). Recognition Task with Feature Selection and Weighted Majority Voting Based on Interval-Valued Fuzzy Sets. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_21
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DOI: https://doi.org/10.1007/978-3-642-34630-9_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34629-3
Online ISBN: 978-3-642-34630-9
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