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 …

Semantics-preserving dimensionality reduction refers to the problem of selecting those input
features that are most predictive of a given outcome; a problem encountered in many areas …

The knowledge discovery process is as old as Homo sapiens. Until some time ago this
process was solely based on the 'natural personal'computer provided by Mother Nature …

Decision makers are often faced with several conflicting alternatives. How do they evaluate
trade-offs when there are more than three criteria? To help people make optimal decisions …

Attribute reduction is one of the most important applications of fuzzy rough sets in machine
learning and pattern recognition. Most existing methods employ the intersection operation of …

Neighborhood rough sets based attribute reduction, as a common dimension reduction
method, has been widely used in machine learning and data mining. Each attribute has the …

There are three basic concepts that underlie human cognition: granulation, organization and
causation. Informally, granulation involves decomposition of whole into parts; organization …

Designed as an introductory level textbook on Artificial Neural Networks at the postgraduate
and senior undergraduate levels in any branch of engineering, this self-contained and well …

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 …

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