Feature selection based on neighborhood rough set is a noteworthy step in dealing with
numerical data. Information entropy, proven in many theoretical analysis and practical …

Feature selection is the most significant pre-processing activity, which intends to reduce the
data dimensionality for enhancing the machine learning process. The evaluation of feature …

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

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 …

Software defect prediction (SDP) is an important research topic in software engineering. It
can optimize the allocation of testing resources by indicating the defect-prone software …

In large-scale decision systems with high dimensions, constructing an efficient feature
selection method via an uncertainty measure, has become a critical problem in fuzzy rough …

The optimal global feature subset cannot be found easily due to the high cost, and most
swarm intelligence optimization-based feature selection methods are inefficient in handling …

Feature selection based on neighborhood rough sets (NRSs) has become a popular area of
research in data mining. However, the limitation that NRSs inherently ignore the differences …

As an important extension of classical rough sets, local rough set model is effective to
handle large data sets with small amounts of labeled data, which has an obvious advantage …

Feature selection benefits machine learning and knowledge acquisition, and it usually
resorts to various intelligent methodologies. Fuzzy rough sets act as a powerful platform of …