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

Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

Tumor classification based on gene expression levels is important for tumor diagnosis.
Since tumor data in gene expression contain thousands of attributes, attribute selection for …

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

Image thresholding based on rough entropy is an efficient image segmentation technique.
The optimal thresholds of the existing exponential and logarithmic rough entropy …

Feature Selection (FS) or Attribute Reduction techniques are employed for dimensionality
reduction and aim to select a subset of the original features of a data set which are rich in the …

The fuzzy set theory and the rough set theory are two distinct but complementary theories
that deal with uncertainty in data. The salient features of both the theories are encompassed …

Each year worldwide, more and more data is collected. In fact, it is estimated that the amount
of data collected and stored at least doubles every 2years. Of this data, a large percentage is …

Abstract Intrusion Detection System (IDS) is one of the most important component of network
defense mechanism. In an attempt to detect network attacks, network traffic features need to …

Rough set theory has been applied successfully in many fields. However, classical rough set
model can only deal with complete and symbolic data sets. Some researchers have …