As a fundamental granular computing strategy, neighborhood granulation has been
acknowledged as an intuitive and effective approach to feature evaluation and selection …

Neighborhood-based attribute reduction plays a vital role in pattern recognition, for selecting
a series of informative and relevant attributes from data sets. The increase in dimensionality …

This paper mainly deals with attribute reduction of inconsistent grey decision systems
(IGDSs) based on the variable precision grey multigranulation rough set (VP-GMGRS) …

Various fuzzy covering based rough set models can characterize and approximate a given
target concept with different lower and upper approximation operators. However, due to the …

Neighborhood rough sets are now widely used to process numerical data. Nevertheless,
most of the existing neighborhood rough sets are not able to distinguish class mixture …

In fields such as pattern recognition and computational intelligence, attribute subset
selection has gradually attracted the attention of researchers as a challenging issue …

In the era of big data, data are being collected, stored, and analyzed at an unprecedented
rate. Owing to the limitations of the quantity, diversity, and complexity of data, traditional data …

K-nearest neighborhood information granularity-based feature selection is derived from the
well-known k-nearest neighbor (k NN) classification technique, which is widely employed in …

Attribute reduction with an accelerator is an efficient strategy to handle large-scale
information systems, but it is rarely suitable for information systems with fuzzy decision …

Optimal granularity selection is a key issue in rough set, by which decision rules can be
generated to assign corresponding decision labels for new samples. The current evaluation …