In this paper, we first review existing fuzzy extensions of the dominance-based rough set approach (DRSA), and advance theory considering additional properties. Moreover, examine application Ordered Weighted Average (OWA) operators to DRSA. OWA have shown a lot potential in handling outliers noisy data decision tables, when they are combined with indiscernibility-based (IRSA). We theoretical pr...

Social media are an essential source of meaningful data that can be used in different tasks such as sentiment analysis and emotion recognition. Mostly, these solved with deep learning methods. Due to the fuzzy nature textual data, we consider using classification methods based on rough sets.Specifically, develop approach for SemEval-2018 detection task, nearest neighbour (FRNN) classifier enhan...

The OWA operators gained interest among researchers as they provide a continuum of aggregation operators able to cover the whole range of compensation between the minimum and the maximum. In some circumstances, it is useful to consider a wider range of values, extending below the minimum and over the maximum. ST-OWA are able to surpass the boundaries of variation of ordinary compensatory operat...

We introduce a new aggregation operator called the heavy ordered weighted averaging – weighted averaging (HOWAWA) operator. It is a new aggregation operator that uses the weighted average and the ordered weighted average in the same formulation and considering the degree of importance that each concept has in the analysis. Moreover, by using heavy aggregations, we are allowing the weighting vec...

We develop a new model for decision making under risk environment and under uncertainty. We introduce a new aggregation operator that unifies the probabilities and the ordered weighted averaging (OWA) operator in the same formulation. We call it the probabilistic ordered weighted averaging (POWA) operator. This aggregation operator provides a more complete representation of the decision problem...

In this paper a class of bottleneck combinatorial optimization problems with uncertain costs is discussed. The uncertainty is modeled by specifying a discrete scenario set containing a finite number of cost vectors, called scenarios. In order to choose a solution the Ordered Weighted Averaging aggregation operator (shortly OWA) is applied. The OWA operator generalizes traditional criteria in de...

Abstract An IFS is suitable way to deal with uncertainty, but operators on it are complex in computation even though they work well. This study presents a C-OWA operator-based method to make aggregation over intuitionistic fuzzy information more easy and convenient, and then applies it to complicated decision making under uncertainty. IFNs or IIFNs are transformed into intervals which are easy ...

Recently, in (Loyer & Straccia 2005) the Any-World Assumption (AWA) has been introduced for normal logic programs as a generalization of the well-known notions of Closed World Assumption (CWA) and the Open World Assumption (OWA). The AWA allows any assignment (i.e. interpretation), over a truth space (bilattice), to be a default assumption and, thus, the CWA and OWA are just special cases. Whil...

We study different types of aggregation operators. We focus on the generalized OWA (GOWA) operator developed by Yager which represents a generalization to a wide range of aggregation operators. We distinguish between aggregations with a descending or with an ascending order. We introduce the induced generalized OWA (IGOWA) operator which represents an extension to the GOWA operator. It generali...