Incremental feature selection approaches can improve the efficiency of feature selection
used for dynamic datasets, which has attracted increasing research attention. Nevertheless …

Neighborhood rough sets provide important insights into dealing with numerical data.
Neighborhood radius, a key factor that affects data uncertainty, is uniformly given in most of …

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 …

Feature selection plays a vital role in many areas of pattern recognition and data mining.
The effective computation of feature selection is important for improving the classification …

Feature selection plays an important role in the classification problems associated with
expert and intelligent systems. The central idea behind feature selection is to identify …

Of all of the challenges which face the effective application of computational intelligence
technologies for pattern recognition, dataset dimensionality is undoubtedly one of the …

Feature selection acts as an important preprocessing method to reduce redundant
information. In order to effectively evaluate the classification information hidden in a given …

Aiming at the problem of effectively selecting relevant features from heterogeneous data
without decision, a novel feature selection approach is studied based on fuzzy mutual …

Feature selection is one of the core contents of rough set theory and application. Since the
reduction ability and classification performance of many feature selection algorithms based …

Abstract We study the Rough Set theory as a method of feature selection based on tolerant
classes that extends the existing equivalent classes. The determination of initial tolerant …