The rough and fuzzy set approaches presented here open up many new frontiers for
continued research and development Computational Intelligence and Feature Selection …

Nearest neighbour (NN) approaches are inspired by the way humans make decisions,
comparing a test object to previously encountered samples. In this paper, we propose an NN …

In this paper, we present a new fuzzy-rough nearest neighbour (FRNN) classification
algorithm, as an alternative to Sarkar's fuzzy-rough ownership function (FRNN-O) approach …

A new fuzzy-rough nearest neighbour (FRNN) classification algorithm is presented in this
paper, as an alternative to Sarkar's fuzzy-rough ownership function (FRNN-O) approach. By …

Fuzzy sets, near sets, and rough sets are useful and important stepping stones in a variety of
approaches to image analysis. These three types of sets and their various hybridizations …

The automated generation of feature pattern-based if-then rules is essential to the success
of many intelligent pattern classifiers, especially when their inference results are expected to …

Using fuzzy-rough hybrids, we have proposed a measure to quantify the functional
dependency of decision attribute (s) on condition attribute (s) within fuzzy data. We have …

In this work, EEG signals were classified by support vector machines to detect whether a
subject's planning to perform a task or not. Various different kernels were utilized to find the …

Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and
has been applied to many real-world problems including data mining, pattern recognition …

This paper provides a broad overview of logical and black box approaches to fuzzy and
rough hybridization. The logical approaches include theoretical, supervised learning, feature …