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An n-ary λ-averaging based similarity classifier

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We introduce a new n-ary λ similarity classifier that is based on a new n-ary λ-averaging operator in the aggregation of similarities. This work is a natural extension of earlier research on similarity based classification in which aggregation is commonly performed by using the OWA-operator. So far λ -averaging has been used only in binary aggregation. Here the λ-averaging operator is extended to the n-ary aggregation case by using t-norms and t-conorms. We examine four different n-ary norms and test the new similarity classifier with five medical data sets. The new method seems to perform well when compared with the similarity classifier
Rocznik
Strony
407--421
Opis fizyczny
Bibliogr. 40 poz., tab., wykr.
Twórcy
autor
  • Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland; Department of Mathematics, Makerere University, P.O. Box 7062, Kampala, Uganda
autor
  • Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland; School of Business and Management, Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland
autor
  • School of Business and Management, Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland
Bibliografia
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  • [4] Dubois, D. and Prade, H. (1985). A review of fuzzy sets aggregation connectives, Information Sciences 36(1): 85–121.
  • [5] Dubois, D. and Prade, H. (2004). On the use of aggregation operations in information fusion processes, Fuzzy Sets and Systems 142(1): 143–161.
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  • [7] Ezghari, S., Belghini, N., Zahi, A. and Zarghili, A. (2015). A gender classification approach based on 3D depth-radial curves and fuzzy similarity based classification, Intelligent Systems and Computer Vision Conference, Fez, Morocco, pp. 1–6.
  • [8] Fengqiu, L. and Xiaoping, X. (2012a). Constructing kernels by fuzzy rules for support vector regressions, International Journal of Innovative Computing, Information and Control 8(7): 4811–4822.
  • [9] Fengqiu, L. and Xiaoping, X. (2012b). Design of natural classification kernels using prior knowledge, IEEE Transactions on Fuzzy Systems 20(1): 135–152.
  • [10] Gabryel, M., Korytkowski, M., Pokropinska, A., Scherer, R. and Drozda, S. (2010). Evolutionary Learning for Neuro-Fuzzy Ensembles with Generalized Parametric Triangular Norms, Springer-Verlag, Berlin/Heidelberg.
  • [11] Gil, G., Girela, L.J., De Juan, J., Gomez-Torres, J.M. and Johnsson, M. (2012). Predicting seminal quality with artificial intelligence methods, Expert Systems with Applications 39(16): 12564–12573.
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  • [13] Klement, E.P.,Mesiar, R. and Pap, E. (2000). Triangular Norms, Kluwer Academic Publishers, Dordrecht.
  • [14] Klement, E.P.,Mesiar, R. and Pap, E. (2003a). Triangular norms, Position paper I: Basic analytical and algebraic properties, Fuzzy Sets and Systems 143(1): 5–26.
  • [15] Klement, E.P.,Mesiar, R. and Pap, E. (2003b). Triangular norms, Position paper II: General constructions and parametrized families, Fuzzy Sets and Systems 145(3): 411–438.
  • [16] Klir, G.J. and Folger, T.A. (1988). Fuzzy Sets, Uncertainty and Information, Prentice Hall, Englewood Cliffs, NJ.
  • [17] Klir, G.J. and Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Englewood Cliffs, NJ.
  • [18] Korytkowski, M. and Scherer, R. (2010). Modular Neurofuzzy Systems Based on Generalized Parametric Triangular Norms, Springer-Verlag, Berlin/Heidelberg.
  • [19] Łukasiewicz, J. (1970). Selected Works, North Holland, Amsterdam/London.
  • [20] Li, P. and Fang, S.C. (2008). On the resolution and optimization of a system of fuzzy relational equations with sup-t composition, Fuzzy Optimization Decision Making 7(2): 169–214.
  • [21] Luukka, P. (2005). Similarity Measure Based Classification, Ph.D. thesis, Lappeenranta University of Technology, Lappeenranta.
  • [22] Luukka, P. (2007). Similarity classifier using similarity measure derived from Yu’s norms in classification of medical data sets, Computers in Biology and Medicine 37(7): 1133–1140.
  • [23] Luukka, P. (2008). Similarity classifier using similarities based on modified probabilistic equivalence relations, Knowledge Based Systems 22(1): 57–62.
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  • [25] Luukka, P. (2011). Feature selection using fuzzy entropy measures with similarity classifier, Expert Systems with Applications 38(4): 4600–4607.
  • [26] Luukka, P. and Kurama, O. (2013). Similarity classifier with ordered weighted averaging operators, Expert Systems with Applications 40(4): 995–1002.
  • [27] Luukka, P. and Leppalampi, T. (2006). Similarity classifier with generalized mean applied to medical data, Computers in Biology and Medicine 36(9): 1026–1040.
  • [28] Luukka, P., Saastamoinen, K. and Kononen, V. (2001). A classifier based on the maximal fuzzy similarity in the generalized Łukasiewicz structure, Proceedings of the FUZZ-IEEE 2001 Conference, Melbourne, Australia, pp. 195–198.
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  • [30] Menger, K. (1942). Statistical metrics, Proceedings of the National Academy of Sciences of the United States of America 28(12): 535–537.
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  • [36] Sivaramakrishnan, R. and Arun, C. (2014). Classification of Denver systems of chromosomes using similarity classifier guided by OWA operators, Current Bioinformatics 9(5): 449–508.
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  • [40] Yager, R.R. (1988). On ordered weighted averaging aggregation operators in multi-criteria decision making, IEEE Transactions on Systems, Man and Cybernetics, 18(1): 183–190.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-11714819-0e9d-4ec9-aaf1-faa2dd563092
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