Modelling uncertainty

Montero, Javier; Ruan, Da

doi:10.1016/j.ins.2009.11.026

Cited by 19 publications

(8 citation statements)

References 36 publications

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“…A promising potential of our approach is its inherent capacity to deal ''in principle" with uncertainty as described by Montero and Ruan [35], Wang [62] by dealing, in particular, with granular data such as intervals and probability/possibility distributions -we remark that the need to accommodate the aforementioned data in practical applications has been acknowledged by different authors including Boukezzoula et al [6], Pedrycz et al [43]. Future work, potentially to be carried out by different authors, may consider the development of analytical formulas in applications including fuzzy subsystem inversion, e.g.…”

Section: Discussionmentioning

confidence: 99%

Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals’ numbers (INs)

Papadakis

Kaburlasos

2010

Information Sciences

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Section: Discussionmentioning

confidence: 99%

Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals’ numbers (INs)

Papadakis

Kaburlasos

2010

Information Sciences

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“…(4). For this aim we use computation with Z-numbers which falls within the province of Computing with words.…”

Section: A Solution To the Problemmentioning

confidence: 99%

“…These approaches require that the objective or subjective probabilities and utility values be exactly known. But in real-world in many cases it becomes impossible to determine the values of objective probabilities [4]. It is more plausible to determine the values of subjective probabilities, reflecting the beliefs of a decision maker.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2014

ijsc

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The concept of decision making under uncertainty is usually associated with information that may be incomplete, not reliable or imprecise, so there are several types of uncertainty. A partial absence of beliefs and fuzziness are some of the aspects of uncertainty. In this paper we consider a somewhat different framework for representing our knowledge. Zadeh suggested a Z-number notion, based on a reliability of the given information. In this study we apply Zinformation to decision making in business problem and suggest the framework for decision making on a base of Z-numbers. The method associates with the construction of a non-additive measure as a lower prevision and uses this capacity in Choquet integral for constructing a utility function. An example of real-world business problem is used to illustrate the proposed approach.

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“…It plays a crucial role in semantic information retrieval systems [1][2][3], sense disambiguation [1,4], and information extraction [3,5]. Due to the uncertainty of the concept, for different people, their cognitions for the same concept are also different [6][7][8][9][10]. To describe various forms of uncertainty, researchers proposed many models: probability models for randomness [11], fuzzy set models for vagueness [12], and rough set models for inconformity and incompleteness [13].…”

Section: Introductionmentioning

confidence: 99%

Uncertain Distribution-Based Similarity Measure of Concepts

Yang

Zhang

et al. 2020

Mathematical Problems in Engineering

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The similarity of concepts is a basic task in the field of artificial intelligence, e.g., image retrieval, collaborative filtering, and public opinion guidance. As a powerful tool to express the uncertain concepts, similarity measure based on cloud model (SMCM) is always utilized to measure the similarity between two concepts. However, current studies on SMCM have two main limitations: (1) the similarity measures based on conceptual intension lack interpretability for merging the numerical characteristics and cannot discriminate some different concepts. (2) The similarity measures based on conceptual extension are always instable and inefficient. To address the above problems, an uncertain distribution-based similarity measure of cloud model (UDCM) is proposed in this paper. By analyzing the definition of the CM, we propose a new complete uncertainty including first-order and second-order uncertainty to calculate the uncertainty more accurately. Then, based on the difference between the complete uncertainty of two concepts, the computing process of UDCM and its some properties are introduced. Finally, we exhibit its advantages by comparing with other methods and verify its validity by experiments.

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Modelling uncertainty

Cited by 19 publications

References 36 publications

Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals’ numbers (INs)

Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals’ numbers (INs)

Untitled

Uncertain Distribution-Based Similarity Measure of Concepts

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