Feature reduction is an important aspect of Big Data analytics on today's ever-larger
datasets. Rough sets are a classical method widely applied in attribute reduction. Most …

In solving some complex conflict problems, compared with intuitionistic fuzzy sets (IFSs) and
Pythagorean fuzzy sets (PFSs), q-rung orthopair fuzzy sets (q-ROFSs) can better express …

This paper presents a novel model that combines several interesting features in relation with
rough sets, namely multi-granularity (which extends Pawlak's single-granular approach), soft …

Notion of soft binary relation is studied here. Some properties of lower and upper
approximations with the help of soft equivalence relations are given. Actually …

This research introduces innovative rough approximation techniques for multidimensional
fuzzy sets by integrating rough sets and multidimensional fuzzy sets. Departing from …

Recommender systems (RS) are substantial for online shopping or digital content services.
However, due to some data characteristics or insufficient historical data, may encounter …

The classical theory of rough sets (RSs) established by Pawlak, mainly focused on the
approximation of sets characterized by a single equivalence relation (ER) over the universe …

The hesitant multiplicative preference relation (HMPR) was initially put forward in 2013.
Utilizing the HMPR, the decision makers can give some possible preference values from the …

In this paper, we discussconsensus measures for typical hesitant fuzzy elements (THFE),
which are the finite and nonempty fuzzy membership degrees under the scope of typical …

In this paper, we first introduce a new type of rough sets called α‐upward fuzzified
preference rodownward fuzzy preferenceugh sets using upward fuzy preference relation …