This paper presents an approach to learn user preferences over numeric and multi-valued linguistic attributes through the analysis of the user selections.
These effects might derive, for example, from some changing cultural or linguistic attributes of individual respondents.
This paper proposes an approach to linguistic multiple attribute group decision making (MAGDM) problem with single-valued neutrosophic 2-tuple linguistic (SVN2TL) assessment information by adding a subjective imprecise estimation of reliability of the 2-tuple linguistic terms (2TLTs).
The size of available offerings also varies (i.e., 1 1000) while linguistic (imprecise) attributes are used to describe offerings, since they require more complex operations than numeric attributes during processing.
Table 3 Factors for determining the relative-value of an object Attribute (linguistic variable) Linguistic values Weight Notes Supply high, moderate, low 0.5 This affects the relative cost of a particular artifact/object.
Table 1 Attributes for determining harvest health Attribute (linguistic variable) Linguistic values Weight Notes Habitat degradation high, normal, low 0.4 This affects ability to get up river and to find spawning beds Harvest mortality high, moderate, low 0.3 Fish caught before they get to the community Catch-per-unit-effort high, normal, low 0.5 How easy are the fish to catch?
(Ethnicity here includes national, cultural, religious, linguistic, or other attributes that are perceived as characteristic of distinct groups).
The attributes, linguistic values and weight applied in Table 1 have been created using knowledge obtained from the available literature.
The attributes, linguistic values and weight applied in Table 1 have been arbitrarily created in consultation with a practitioner in the field.
The psychometric properties of OHRQoL inventories depend largely on the linguistic and cultural attributes of the population under study.
