Feature selection attempts to capture the more discriminative features and plays a significant
role in multilabel learning. As an efficient mathematical tool to handle incomplete and …

The density-based method is a more widely used anomaly detection. However, most of the
existing density-based methods mainly focus on dealing with certainty data and do not …

Unsupervised anomaly detection is an important research direction in the process of
unsupervised knowledge acquisition. It has been successfully applied in many fields, such …

Unsupervised attribute reduction becomes very challenging due to a lack of decision
information, which is to select a subset of attributes that can maintain learning ability without …

Feature selection, as an effective dimensionality reduction technique, is favored in
preprocessing data. However, most existing algorithms are solely liable for labeled or …

Variable precision (fuzzy) rough sets are interesting generalizations of Pawlak rough sets
and can handle uncertain and imprecise information well due to their error tolerance …

Unsafe acts of seafarers have been widely recognized as the direct causes of maritime
accidents. Previous studies have demonstrated the significant role of unsafe acts, and …

Feature selection has aroused extensive attention and aims at selecting features that are
highly relevant to classification from raw datasets to improve the performance of a learning …

Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …

Attribute reduction is an important application of rough set theory. Most existing rough set
models do not consider the weight information of attributes in information systems. In this …