Fuzzy rough set theory is a powerful tool to deal with uncertainty information, which has
been successfully applied to the fields of attribute reduction, rule extraction, classification …

Feature selection in the data with different types of feature values, ie, the heterogeneous or
mixed data, is especially of practical importance because such types of data sets widely …

Feature selection is a popular technique of preprocessing data. In order to deal with
dynamic or large data, incremental feature selection has been developed, in which the …

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 …

The ontology-based information systems (IS) development is beneficial for analyzing,
conceptual modeling, designing, and re-engineering complex IS to be semantically enriched …

The package RoughSets, written mainly in the R language, provides implementations of
methods from the rough set theory (RST) and fuzzy rough set theory (FRST) for data …

Data reduction, aiming to reduce the original data by selecting the most representative
information, is an important technique of preprocessing data. At present, large-scale or huge …

In many data mining processes, neighborhood operators play an important role as they are
generalizations of equivalence classes which were used in the original rough set model of …

Fuzzy covering-based rough set models are hybrid models using both rough set and fuzzy
set theory. The former is often used to deal with uncertain and incomplete information, while …

A fuzzy relation information system may be viewed as an information system with fuzzy
relations. Uncertainty measurement is a critical evaluating tool. This paper investigates …