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 …

The volume of data on the social media is huge and even keeps increasing. The need for
efficient processing of this extensive information resulted in increasing research interest in …

Attribute reduction is one of the biggest challenges encountered in computational
intelligence, data mining, pattern recognition, and machine learning. Effective in feature …

Attribute subset selection is an important issue in data mining and information processing.
However, most automatic methodologies consider only the relevance factor between …

Rough set theory, as one of the most useful soft computing methods dealing with vague and
uncertain information, has been successfully applied to many fields, and one of its main …

Rough sets, especially fuzzy-rough sets, have proven to be a powerful tool for dealing with
vagueness and uncertainty in data analysis. Fuzzy-rough feature selection has been shown …

The aim of this paper is to introduce the notion of a rough soft hemiring, which is an
extended notion of a rough hemiring and a soft hemiring. We study roughness in soft …

The original rough set model, ie, Pawlak's single-granulation rough set model has been
extended to a multigranulation rough set model, where two kinds of multigranulation …

The relative decision self-information is a crucial evaluation function of feature selection in
information system. It encapsulates classification information in upper and lower …

Feature selection for mixed and incomplete data in terms of numerical and categorical
features with missing values has currently gained considerable attention. The development …