Feature reduction is an important aspect of Big Data analytics on today's ever-larger
datasets. Rough sets are a classical method widely applied in attribute reduction. Most …

Attribute reduction with variable precision rough sets (VPRS) attempts to select the most
information-rich attributes from a dataset by incorporating a controlled degree 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 …

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 …

Feature selection is a challenging problem in areas such as pattern recognition, machine
learning and data mining. Considering a consistency measure introduced in rough set …

Attribute reduction is nowadays an extremely important data preprocessing technique in the
field of data mining, which has gained much attention due to its ability to provide better …

Feature selection (attribute reduction) from large-scale incomplete data is a challenging
problem in areas such as pattern recognition, machine learning and data mining. In rough …

The k-nearest-neighbor rule is a popular classification technique, and rough set theory is an
effective mathematical tool to deal with the uncertainty of data. Rough set models based on k …

With the advent of the age of big data, a typical big data set called limited labeled big data
appears. It includes a small amount of labeled data and a large amount of unlabeled data …

Attribute reduction is one of the most important research issues in the rough set theory. The
purpose of attribute reduction is to find a minimal attribute subset that satisfies some specific …