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2003 | 13 | 2 | 215-223

Tytuł artykułu

A fuzzy if-then rule-based nonlinear classifier

Autorzy

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
This paper introduces a new classifier design method that is based on a modification of the classical Ho-Kashyap procedure. The proposed method uses the absolute error, rather than the squared error, to design a linear classifier. Additionally, easy control of the generalization ability and robustness to outliers are obtained. Next, an extension to a nonlinear classifier by the mixture-of-experts technique is presented. Each expert is represented by a fuzzy if-then rule in the Takagi-Sugeno-Kang form. Finally, examples are given to demonstrate the validity of the introduced method.

Rocznik

Tom

13

Numer

2

Strony

215-223

Opis fizyczny

Daty

wydano
2003
otrzymano
2002-05-28
poprawiono
2002-08-29

Twórcy

  • Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

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  • Bezdek J.C. (1982): Pattern Recognition with Fuzzy Objective Function Algorithms. - New York: Plenum Press.
  • Bezdek J.C. and Pal S.K. (Eds.) (1992): Fuzzy Models for Pattern Recognition. - New York: IEEE Press.
  • Bezdek J.C., Reichherzer T.R., Lim G.S. and Attikiouzel Y.(1998): Multiple-prototype classifier design. - IEEE Trans.Syst. Man Cybern., Part C, Vol. 28, No. 1, pp. 67-78.
  • Czogal a E. and L ęski J.M. (2000): Fuzzy and Neuro-Fuzzy Intelligent Systems. - Heidelberg: Physica-Verlag.
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  • Ho Y.-C. and Kashyap R.L. (1965): An algorithmfor linear inequalities and its applications. - IEEE Trans. Elec. Comp., Vol. 14, No. 5, pp. 683-688.
  • Ho Y.-C. and Kashyap R.L. (1966): A class of iterative procedures for linear inequalities. - J. SIAM Contr., Vol. 4, No. 2, pp. 112-115.
  • Ishibuchi H., Nakashima T. and Murata T. (1999): Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. - IEEE Trans. Syst. Man Cybern., Part B, Vol. 29, No. 5, pp. 601-618.
  • Huber P.J. (1981): Robust Statistics. - New York: Wiley.
  • Keller J.M., Gray M.R. and Givens J.A. (1985): Afuzzy k-nearest neighbors algorithm. - IEEE Trans. Syst. Man Cybern., Vol. 15, No. 3, pp. 580-585.
  • Krishnapuram R. and Keller J.M. (1993): A possibilistic approach to clustering. - IEEE Trans. Fuzzy Syst., Vol. 1, No. 2, pp. 98-110.
  • Kim E., Park M., Ji S. and Park M. (1997): A new approach to fuzzy modeling. - IEEE Trans. Fuzzy Syst., Vol. 5, No. 3, pp. 328-337.
  • Kuncheva L.I. and Bezdek J.C. (1999): Presupervised and postsupervised prototype classifier design. - IEEE Trans. Neural Netw., Vol. 10, No. 5, pp. 1142-1152.
  • Kuncheva L.I. (2000a): How good are fuzzy if-then classifiers? - IEEE Trans. Syst. Man Cybern., Part B, Vol. 30, No. 4, pp. 501-509.
  • Kuncheva L.I. (2000b): Fuzzy Classifier Design.- Heidelberg: Physica-Verlag.
  • Kuncheva L.I. (2001): Using measures of similarity and inclusion for multiple classifier fusion by decision templates. -Fuzzy Sets Syst., Vol. 122, No. 3, pp. 401-407.
  • Kuncheva L.I. (2002): Switching between selection and fusion in combining classifiers: An experiment. - IEEE Trans. Syst. Man Cybern.. Part B, Vol. 32, No. 2, pp. 146-156.
  • Łęski J. and Henzel N. (2001): A neuro-fuzzy system based on logical interpretation of if-then rules, In: Fuzzy Learning and Applications (Russo M. and Jain L.C., Eds.). - New York: CRC Press, pp. 359-388.
  • Łęski J. (2002): Robust weighted averaging.- IEEE Trans. Biomed. Eng., Vol. 49, No. 8, pp. 796-804.
  • Malek J.E., Alimi A.M. and Tourki R. (2002): Problems in pattern classification in high dimensional spaces: Behavior of aclass of combined neuro-fuzzy classifiers. - Fuzzy Sets Syst., Vol. 128, No. 1, pp. 15-33.
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  • Nath A.K. and Lee T.T. (1982): On the design of a classifier with linguistic variables as inputs. - Fuzzy Sets Syst., Vol. 11, No. 2, pp. 265-286.
  • Ripley B.D. (1996): Pattern Recognition and Neural Networks. - Cambridge: Cambridge University Press.
  • Runkler T.A. and Bezdek J.C. (1999): Alternating cluster estimation: A new tool for clustering and function approximation.- IEEE Trans. Fuzzy Syst., Vol. 7, No. 4, pp. 377-393.
  • Rutkowska D. (2002): Neuro-Fuzzy Architectures and Hybrid Learning. - Heidelberg: Physica-Verlag.
  • Setnes M. and Babuvska R. (1999): Fuzzy relational classifier trained by fuzzy clustering. - IEEE Trans. Syst. Man Cybern., Part B, Vol. 29, No. 5, pp. 619-625.
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