An approach to group decision making under uncertainty with application to project selection

Sotirov, G.; Krasteva, Evdokia B.

doi:10.1007/bf02032480

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Clearly the positive neighborhood function of information cell L i is similar to the membership function of fuzzy set. Hence we can use information cells to represent vague concepts in many uncertain situations such as group decision making (Sotirov and Krasteva 1994) and information aggregation (Tang 2008).…”

Section: Information Cell For Cognitive Representation Of Vague Conceptmentioning

confidence: 99%

Information cells and information cell mixture models for concept modelling

Tang

Lawry

2012

Ann Oper Res

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By combining the prototype theory and random set theory interpretations of vague concepts, a novel structure named information cell and a combined structure named information cell mixture model are proposed to represent the semantics of vague concepts. An information cell L i on the domain Ω has a transparent cognitive structure 'L i = about P i ' which is mathematically formalized by a 3-tuple P i , d i , δ i comprising a prototype set P i (⊆ Ω), a distance function d i on Ω and a density function δ i on [0, +∞). An information cell mixture model on domain Ω is actually a set of weighted information cells L i s. A positive neighborhood function of the information cell mixture model is introduced in this paper to reflect the belief distribution of positive neighbors of the underlying concept. An information cellularization algorithm is also proposed to learn the information cell mixture model from a training data set, which is a direct application of the k-means and EM algorithms. Information cell mixture models provide some tools for information coarsening and concept modelling, and have potential applications in uncertain reasoning and classification.

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Section: Information Cell For Cognitive Representation Of Vague Conceptmentioning

confidence: 99%

Information cells and information cell mixture models for concept modelling

Tang

Lawry

2012

Ann Oper Res

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“…On the other hand, in the case of complex problems, it is usually better to use opinions of a group of experts because it is difficult for a single person to possess knowledge and experience in all details of the problem (Sotirov, Krasteva 1994).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Model for Extending Brand Based on Fuzzy Aras and Anp Methods

Zamani

Rabbani

Yazdani-Chamzini

et al. 2014

Journal of Business Economics and Management

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Abstract. Brand extension is one of the most popular strategies in marketing. This is due to the fact that consumers usually take into account the brand first because it indirectly reflects the design, quality, and functions. According to the significant role of brand in creating benefit and diminishing risk for a new product, this study intended to develop a novel model for selecting the most appropriate strategy in brand extension. However, there are a number of criteria influencing the possible strategies, and they often are interdependent. Therefore, a multi-criteria decision making model based on Analytic Network Process and Additive Ratio Assessment methods has been developed to systematically clarify the interdependent relationships among the evaluation criteria of brand extension and then, scientifically evaluate the feasible strategies and rank the priorities of brand extension strategies. The results of the proposed model show that "Production cost" is the most important factor, followed by "Quality of parent brand" and "Perceived risk" while "Ice cream" is the highest satisfaction of brand extension.

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Information Cells and Information Cell Mixture Models

Qin

Tang

2014

Uncertainty Modeling for Data Mining

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An approach to group decision making under uncertainty with application to project selection

Cited by 7 publications

References 19 publications

Information cells and information cell mixture models for concept modelling

Information cells and information cell mixture models for concept modelling

An Integrated Model for Extending Brand Based on Fuzzy Aras and Anp Methods

Information Cells and Information Cell Mixture Models

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