Triangular intuitionistic fuzzy numbers (TIFNs) is a special case of intuitionistic fuzzy (IF) set and the ranking of TIFNs is an important problem. The aim of this paper is to develop a new methodology for ranking TIFNs by using multiattribute decision making methods (MADM). In this methodology, the value and ambiguity indices of TIFNs may be considered as the attributes and the TIFNs in compa...

Ranking of alternatives is a common, difficult and repeatedly addressed problem, especially when it requires negotiation of experts. The celebrated Arrow’s impossibility theorem expresses formally its difficulty. In spite of theprogressmadebyadopting soft ranking, the problem is far from being generically solved. The paper provides a, probably novel, problem formulation by viewing ranking of al...

A modification of a fuzzy classification algorithm is shown in this paper. This algorithm classifies multiple-criteria fuzzy or crisp elements and the task of the classification is traced back to ranking of elements, as well as to learning weight numbers similarly to neural networks. Two new particular versions of the algorithm can be utilized in the multiple-criteria decision-making. If we hav...

Analytic hierarchy process (AHP) is a frequently used method for ranking alternatives. The alternatives to be ranked are modeled based on a set of criteria. The ranking computed by AHP is trusted on faith notwithstanding the fact that a model is an approximation and liable to failure. We present a model to estimate the trust in the AHP ranking. The model helps in determining if it is prudent to...

Preference learning is an emerging subfield of machine learning, which deals with the induction of preference models from observed or revealed preference information [2]. Such models are typically used for prediction purposes, for example, to predict context-dependent preferences of individuals on various choice alternatives. Depending on the representation of preferences, individuals, alternat...

The purpose of this paper is to extend a new balancing and ranking method to handle uncertainty for a multiple attribute analysis under a hesitant fuzzy environment. The presented hesitant fuzzy balancing and ranking (HF-BR) method does not require attributes’ weights through the process of multiple attribute decision making (MADM) under hesitant conditions. For the rating of possible alternati...

Reasoning about agent preferences on a set of alternatives, and the aggregation of such preferences into some social ranking is a fundamental issue in reasoning about multi-agent systems. When the set of agents and the set of alternatives coincide, we get the ranking systems setting. A famous type of ranking systems are page ranking systems in the context of search engines. Such ranking systems...