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 …

Neighborhood rough sets are widely used as an effective tool to deal with numerical data.
However, most of the existing neighborhood granulation models cannot well describe the …

The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …

Multi-label feature selection has grabbed intensive attention in many big data applications.
However, traditional multi-label feature selection methods generally ignore a real-world …

The scale of the radius for constructing neighborhood relation has a great effect on the
results of neighborhood rough sets and corresponding measures. A very small radius …

Variations of reservoir water level and seasonal precipitation have resulted in significant
movement and destabilization of landslides in the Three Gorges Reservoir (TGR) region of …

In the field of rough set, how to improve the efficiency of obtaining reduct has been paid
much attention to. One of the typical strategies is to reduce the number of comparisons …

This chapter emphasizes on the role played by rough set theory (RST) within the broad field
of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm …

Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the
high dimensionality, gene selection (finding a small, closely related gene set to accurately …

With the development of bioinformatics, tumor classification from gene expression data
becomes an important useful technology for cancer diagnosis. Since a gene expression …