The assignment of alternatives (observations/objects) into predefined homogenous groups
is a problem of major practical and research interest. This type of problem is referred to as …

Probabilistic approaches have been applied to the theory of rough set in several forms,
including decision-theoretic analysis, variable precision analysis, and information-theoretic …

The original rough set approach proved to be very useful in dealing with inconsistency
problems following from information granulation. It operates on a data table composed of a …

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 …

We propose a new feature selection strategy based on rough sets and particle swarm
optimization (PSO). Rough sets have been used as a feature selection method with much …

Data mining is a powerful new technique to help companies mining the patterns and trends
in their customers data, then to drive improved customer relationships, and it is one of well …

A rapid growth of interest in rough set theory [297] and its applications can be lately seen in
the number of international workshops, conferences and seminars that are either directly …

In this article we are considering a multicriteria classification that differs from usual
classification problems since it takes into account preference orders in the description of …

Rough set theory and belief function theory, two popular mathematical frameworks for
uncertainty representation, have been widely applied in different settings and contexts …

We consider a sorting (classification) problem in the presence of multiple attributes and
criteria, called the MA&C sorting problem. It consists in assignment of some actions to some …