Fuzzy implication operators for difference operations for fuzzy sets and cardinality-based measures of comparison

Fono, Louis Aimé; Gwet, Henri; Bouchon‐Meunier, Bernadette

doi:10.1016/j.ejor.2006.10.001

Cited by 12 publications

(19 citation statements)

References 10 publications

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“…Measuring similarity of uncertainty based information has attracted a lot of attention in probability theory [4,17], in belief function theory [8,14,21,26], in fuzzy set theory [3,6,9,23,24] and in credal set theory [1]. This is not the case for possibilistic uncertain information, in fact, few works have been done in this direction.…”

Section: Measuring Similarity Of Possibilistic Uncertain Informationmentioning

confidence: 97%

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Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information

Jenhani

Amor

Elouedi

et al. 2007

Lecture Notes in Computer Science

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Abstract. This paper addresses the issue of measuring similarity between pieces of uncertain information in the framework of possibility theory. In a first part, natural properties of such functions are proposed and a survey of the few existing measures is presented. Then, a new measure so-called Information Affinity is proposed to overcome the limits of the existing ones. The proposed function is based on two measures, namely, a classical informative distance, e.g. Manhattan distance which evaluates the difference, degree by degree, between two normalized possibility distributions and the well known inconsistency measure which assesses the conflict between the two possibility distributions. Some potential applications of the proposed measure are also mentioned in this paper.

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Section: Measuring Similarity Of Possibilistic Uncertain Informationmentioning

confidence: 97%

“…In [21], the authors proposed a distance for the quantification of errors resulting from basic probability assignment approximations. Similarity measures between two fuzzy sets A and B have been also proposed in the literature [6] [9] [23] [24]. For instance, in the work by Bouchon-Meunier and al.…”

Section: Introductionmentioning

confidence: 99%

Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information

Jenhani

Amor

Elouedi

et al. 2007

Lecture Notes in Computer Science

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“…We end this section with the review on regular fuzzy strict components of a given FWPR (see [11,13,14,16]). Fono and Andjiga [11] determined for any t-conorm S, a class of regular fuzzy strict preferences of a given FWPR R. Each class has a minimal element called the minimal regular fuzzy strict preference of R associated to S and defined as follows:…”

Section: Definitionmentioning

confidence: 99%

On strict lower and upper sections of weakly complete fuzzy pre-orders based on co-implication

Fono

Gwet

Fotso

2008

Fuzzy Sets and Systems

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“…Thus, it perfectly fits our system. For processing convenience, this theory is related to other theories [6,9,10,11], such as, probability theory, fuzzy set theory and belief function theory. The crossing between one theory to other, via existing transformation rules is often accompanied by a loss of information, i.e., an increase of imperfection.…”

Section: Introductionmentioning

confidence: 99%

Possibility theory based approach for iris recognition in biometric systems

Bellaaj

Kallel²,

Masmoudi³

2014

2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

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Iris recognition could be among reliable means and useful features for advanced biometry technics. In this work, we propose an efficient and novel scheme of one biometric advanced tool using one single modality, i.e the iris, and based on the possibility theory. Such novel tool could easily encompass the variant forms of imperfections that could affect iris data, so reinforcing then the performance of the related recognition system. Accordingly, the use of the possibility theory could be so an attractive tool for modeling such data with its variant forms. Experimental results on identity verification show that the proposed system with possibility theory yields interesting results especially when imperfect biometric data was involved, and have satisfactory performance and robustness in terms of identification.

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Fuzzy implication operators for difference operations for fuzzy sets and cardinality-based measures of comparison

Cited by 12 publications

References 10 publications

Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information

Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information

On strict lower and upper sections of weakly complete fuzzy pre-orders based on co-implication

Possibility theory based approach for iris recognition in biometric systems

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