In this paper, we focus our discussion on the rough set-based partitioning attribute selection.
Firstly, we point out that the statement of MMR technique is an extension of Mazlack's …

Attribute reduction is the strongest and most characteristic result in rough set theory to
distinguish itself to other theories. In the framework of rough set, an approach of discernibility …

Rough set theory is a useful mathematical tool to deal with vagueness and uncertainty in
available information. The results of a rough set approach are usually presented in the form …

For decision-theoretic rough sets, a key issue is determining the thresholds for the
probabilistic rough set model by setting appropriate cost functions. However, it is not easy to …

Dimensional reduction has been a major problem in data mining problems. In many real
time situations, eg database applications and bioinformatics, there are far too many …

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 …

Rough set theory (RS) has been a topic of general interest in the field of knowledge
discovery and pattern recognition. Machine learning algorithms are known to degrade in …

Practical machine learning algorithms are known to degrade in performance when faced
with many features that are not necessary for rule discovery. To cope with this problem …

Rough Set is a valid mathematical theory developed in recent years, which has the ability to
deal with imprecise, uncertain, and vague information. It has been applied in such fields as …