On the relevance of some families of fuzzy sets

In this paper we stress the relevance of a particular family of fuzzy sets, where each element can be viewed as the result of a classification problem. In particular, we assume that fuzzy sets are defined from a well-defined universe of objects into a valuation space where a particular graph is being defined, in such a way that each element of the considered universe has a degree of membership with respect to each state in the valuation space. The associated graph defines the structure of such a valuation space, where an ignorance state represents the beginning of a necessary learning procedure. Hence, every single state needs a positive definition, and possible queries are limited by such an associated graph. We then allocate this family of fuzzy sets with respect to other relevant families of fuzzy sets, and in particular with respect to Atanassov's intuitionistic fuzzy sets. We postulate that introducing this graph allows a natural explanation of the different visions underlying Atanassov's model and interval valued fuzzy sets, despite both models have been proven equivalent when such a structure in the valuation space is not assumed.

[1]  J. Montero,et al.  Representation of consistent recursive rules , 2001, Eur. J. Oper. Res..

[2]  Janusz Kacprzyk,et al.  Computing with Words in Information/Intelligent Systems 1 , 1999 .

[3]  Gaspar Mayor,et al.  Aggregation Operators , 2002 .

[4]  Jang-Suk Park,et al.  A new fuzzy resolution principle based on the antonym , 2000, Fuzzy Sets Syst..

[5]  R. Mesiar,et al.  Aggregation operators: properties, classes and construction methods , 2002 .

[6]  Madan M. Gupta,et al.  Fuzzy Logic in Knowledge-Based Systems, Decision and Control , 1988 .

[7]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[8]  Javier Montero,et al.  A graph coloring approach for image segmentation , 2007 .

[9]  Bernard De Baets,et al.  T -partitions , 1998 .

[10]  Etienne E. Kerre,et al.  On the relationship between some extensions of fuzzy set theory , 2003, Fuzzy Sets Syst..

[11]  E. Trillas Sobre funciones de negación en la teoría de conjuntos difusos. , 1979 .

[12]  J. Montero,et al.  A Survey of Interval‐Valued Fuzzy Sets , 2008 .

[13]  Chris Cornelis,et al.  On the representation of intuitionistic fuzzy t-norms and t-conorms , 2004, IEEE Transactions on Fuzzy Systems.

[14]  Enric Trillas,et al.  On the use of words and fuzzy sets, , 2006, Inf. Sci..

[15]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[16]  J. Bezdek,et al.  Fuzzy partitions and relations; an axiomatic basis for clustering , 1978 .

[17]  J. Goguen L-fuzzy sets , 1967 .

[18]  Gregory S. Biging,et al.  Relevance and redundancy in fuzzy classification systems , 2001 .

[19]  J. A. Robinson,et al.  A Machine-Oriented Logic Based on the Resolution Principle , 1965, JACM.

[20]  Javier Montero,et al.  Searching for the dimension of valued preference relations , 2003, Int. J. Approx. Reason..

[21]  H. B. Mitchell Ranking type-2 fuzzy numbers , 2006, IEEE Transactions on Fuzzy Systems.

[22]  Robert Ivor John,et al.  Type-2 Fuzzy Logic and the Modelling of Uncertainty , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[23]  Humberto Bustince,et al.  Vague sets are intuitionistic fuzzy sets , 1996, Fuzzy Sets Syst..

[24]  Madan M. Gupta,et al.  Introduction to Fuzzy Arithmetic , 1991 .

[25]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[26]  Vincenzo Cutello,et al.  Fuzzy classification systems , 2004, Eur. J. Oper. Res..

[27]  G. Shafer Savage revisited , 1990 .

[28]  Janusz Kacprzyk,et al.  Distances between intuitionistic fuzzy sets , 2000, Fuzzy Sets Syst..

[29]  Carita Paradis,et al.  Antonymy and negation—The boundedness hypothesis , 2006 .

[30]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[31]  Chris Cornelis,et al.  Advances and challenges in interval-valued fuzzy logic , 2006, Fuzzy Sets Syst..

[32]  F J Montero de Juan Comprehensive fuzziness , 1986 .

[33]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning - II , 1975, Inf. Sci..

[34]  Satoko Titani,et al.  Intuitionistic fuzzy logic and intuitionistic fuzzy set theory , 1984, Journal of Symbolic Logic.

[35]  Ion Iancu,et al.  Connectives for fuzzy partitions , 1999, Fuzzy Sets Syst..

[36]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[37]  Javier Montero,et al.  Crisp Acts, Fuzzy Decisions , 1998 .

[38]  Javier Montero,et al.  A discussion on aggregation operators , 2004, Kybernetika.

[39]  Gianpiero Cattaneo,et al.  Basic intuitionistic principles in fuzzy set theories and its extensions (A terminological debate on Atanassov IFS) , 2006, Fuzzy Sets Syst..

[40]  Vincenzo Cutello,et al.  Recursive connective rules , 1999, Int. J. Intell. Syst..

[41]  K. Atanassov More on intuitionistic fuzzy sets , 1989 .

[42]  Jee-Hyong Lee,et al.  A method for ranking fuzzy numbers and its application to decision-making , 1999, IEEE Trans. Fuzzy Syst..

[43]  Dan Dumitrescu,et al.  Fuzzy partitions with the connectives T∞, S∞ , 1992 .

[44]  Krassimir T. Atanassov,et al.  Answer to D. Dubois, S. Gottwald, P. Hajek, J. Kacprzyk and H. Prade's paper "Terminological difficulties in fuzzy set theory - the case of "Intuitionistic Fuzzy Sets" , 2005, Fuzzy Sets Syst..

[45]  Vilém Novák,et al.  Antonyms and linguistic quantifiers in fuzzy logic , 2001, Fuzzy Sets Syst..

[46]  Ronald R. Yager,et al.  Uninorm aggregation operators , 1996, Fuzzy Sets Syst..

[47]  Chris Cornelis,et al.  Implication in intuitionistic fuzzy and interval-valued fuzzy set theory: construction, classification, application , 2004, Int. J. Approx. Reason..

[48]  F. J. Juan,et al.  Extensive fuzziness , 1987 .

[49]  Sukhamay Kundu,et al.  Fuzzy logic or Lukasiewicz logic: A clarification , 1998, Fuzzy Sets Syst..

[50]  D. Butnariu Additive fuzzy measures and integrals, III , 1983 .

[51]  Didier Dubois,et al.  Terminological difficulties in fuzzy set theory - The case of "Intuitionistic Fuzzy Sets" , 2005, Fuzzy Sets Syst..

[52]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[53]  Javier Montero,et al.  Soft dimension theory , 2003, Fuzzy Sets Syst..

[54]  Javier Montero,et al.  A coloring fuzzy graph approach for image classification , 2006, Inf. Sci..

[55]  Javier Montero,et al.  A Coloring Algorithm for Image Classification , 2004 .

[56]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[57]  Radko Mesiar,et al.  Triangular Norms , 2000, Trends in Logic.

[58]  I. B. Turksen,et al.  A model for the measurement of membership and the consequences of its empirical implementation , 1984 .

[59]  Javier Montero,et al.  Accuracy measures for fuzzy classification in remote sensing , 2006 .

[60]  Krassimir T. Atanassov,et al.  My Personal View on Intuitionistic Fuzzy Sets Theory , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[61]  Jerry M. Mendel,et al.  Advances in type-2 fuzzy sets and systems , 2007, Inf. Sci..

[62]  Javier Montero,et al.  Preferences, classification and intuitionistic fuzzy sets , 2003, EUSFLAT Conf..

[63]  Philippe Fortemps,et al.  A Graded Quadrivalent Logic for Ordinal Preference Modelling: Loyola–Like Approach , 2002, Fuzzy Optim. Decis. Mak..

[64]  Humberto Bustince,et al.  Structures on intuitionistic fuzzy relations , 1996, Fuzzy Sets Syst..

[65]  Sukhamay Kundu,et al.  Fuzzy Logic or Lukasiewicz Logic: A Clarification , 1994, ISMIS.

[66]  I. Turksen Interval valued fuzzy sets based on normal forms , 1986 .

[67]  Petr Cintula Basics of a formal theory of fuzzy partitions , 2005, EUSFLAT Conf..