ABSTRACT
In the symbolic approach, linguistic labels are directly computed by considering the nature of the linguistic assessment without considering the semantic (meaning) of the labels. The approximation approach deals with the arithmetic of associated membership functions or fuzzy numbers of the fuzzy sets accompanied with the linguistic values. In this chapter, the authors produce factor-based models which have been developed via the above-mentioned two approaches. They only consider the ordered symmetrically distributed linguistic terms sets. In this method, the authors develop factor-based models by imposing several linguistic aggregation operators such as Linguistic Ordered Weighted Average, Linguistic Weighted Average, and Majority guided Linguistic Induced Ordered Weighted Average. There are situations in which the importance of a parameter for the problem is different from parameter to parameter. Due to the differences between cardinalities and semantics, direct computation of labels is un-executable; however, the evaluation is possible with the semantic(meaning) of the labels.
