In this study, we establish an interesting connection between two mathematical approaches
to vagueness: rough sets and soft sets. Soft set theory is utilized, for the first time, to …

We propose a framework for the study of covering based rough set approximations. Three
equivalent formulations of the classical rough sets are examined by using equivalence …

Feature subset selection is viewed as an important preprocessing step for pattern
recognition, machine learning and data mining. Most of researches are focused on dealing …

In this paper, we contribute to a recent and successful modelization of uncertainty, which the
practitioner often encounters in the formulation of multicriteria group decision making …

This paper presents a hybrid filter–wrapper feature subset selection algorithm based on
particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter …

In many data mining and machine learning applications, there are two objectives in the task
of classification; one is decreasing the test cost, the other is improving the classification …

Fuzzy coverings are a natural extension of the coverings by replacing crisp sets with fuzzy
sets. Recently, an excellent introduction to the definition of a fuzzy β-covering is due to Ma …

Recently, a multigranulation rough set (MGRS) has become a new direction in rough set
theory, which is based on multiple binary relations on the universe. However, it is worth …

Recently, by combining rough set theory with granular computing, pessimistic and optimistic
multigranulation rough sets have been proposed to derive “AND” and “OR” decision rules …

Recommender systems employ recommendation algorithms to predict users' preferences to
items. These preferences are often represented as numerical ratings. However, existing …