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 …

Attribute reduction is one of the most important preprocessing steps in machine learning and
data mining. As a key step of attribute reduction, attribute evaluation directly affects …

The ontology-based information systems (IS) development is beneficial for analyzing,
conceptual modeling, designing, and re-engineering complex IS to be semantically enriched …

In this study, a granular ball based selector was developed for reducing the dimensions of
data from the perspective of attribute reduction. The granular ball theory offers a data …

In terms of neighborhood rough sets, the tri-level granular structure of neighborhood system
(carrying the neighborhood granule, swarm, and library) establishes a granular computing …

Attribute reduction, also called feature selection, is one of the most important issues of rough
set theory, which is regarded as a vital preprocessing step in pattern recognition, machine …

Most existing results about attribute reduction are reported by considering one and only one
granularity, especially for the strategies of searching reducts. Nevertheless, how to derive …

Rough sets have been widely used in the fields of machine learning and feature selection.
However, the classical rough sets have the problems of difficultly dealing with real-value …

Ensemble learning is an effective approach for improving the generalization ability of base
classifiers. To generate a set of accurate and diverse base classifiers, different data …

Granular computing is an umbrella term to cover a series of theories, methodologies,
techniques, and tools that make use of information granules in complex problem solving …