Feature selection using variable precision neighborhood rough sets (VPNRS) has garnered
considerable attention in data mining and knowledge discovery. Nevertheless, the positive …

The decision tree algorithm has been widely used in data mining and machine learning due
to its high accuracy, low computational cost and high interpretability. However, when dealing …

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 …

As a useful extension of rough sets, multigranulation rough sets (MGRSs) can be used to
deal with a variety of complex data. Numerous significant advances have been achieved by …

The performance of the neighborhood rough set model in feature selection is limited by
nonobjective parameter selection method, the uncertainty measures considered only from a …

The neighborhood rough set model (NRS) has been widely applied to study feature
selection. Nevertheless, the dependency, as a significant feature evaluation function in NRS …

The classical ID3 decision tree algorithm cannot directly handle continuous data and has a
poor classification effect. Moreover, most of the existing approaches use a single …

Network intrusion detection system (NIDS) is a key component to identify abnormal behavior
of network systems and plays an important role in preventing the occurrence of network …

Many researchers have proposed algorithms to improve the network performance of
vehicular ad hoc network (VANET) clustering techniques for different applications. The …

In practice, people may hesitate to evaluate uncertain things. As an extension of fuzzy sets,
intuitionistic hesitant fuzzy sets use multiple membership and non-membership degrees to …