In the field of rough set, how to improve the efficiency of obtaining reduct has been paid
much attention to. One of the typical strategies is to reduce the number of comparisons …

In the fields of rough set and machine learning, attribute reduction has been demonstrated to
be effective in removing redundant attributes with clear explanations. Therefore, not only the …

Exploring feature significance associated with label is a fundamental task in the architecture
of feature selection. Nevertheless, most of the existing schemes are limited by the global …

Outlier detection is a crucial task for identifying unexpected patterns, errors, and behaviors;
therefore, maximizing the valuable information obtained from ubiquitous, incomplete …

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 …

Presently, attribute reduction, as one of the most important topics in the field of rough set,
has been widely explored from different perspectives. To derive the qualified reduct defined …

As an important uncertainty reasoning method, Dempster-Shafer (DS) evidence theory has
been widely applied to expert system, comprehensive evaluation, information fusion and …

Feature selection is one basic and critical technology for data mining, especially in current
“big data era”. Rough set theory (RST) is sensitive to noise in feature selection due to the …

Telecom fraud detection is of great significance in online social networks. Yet the massive,
redundant, incomplete, and uncertain network information makes it a challenging task to …

Due to the high cost of labelling data, a lot of partially hybrid data are existed in many
practical applications. Uncertainty measure (UM) can supply new viewpoints for analyzing …