Abstract Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary
subject, and is referred to as, multi-sensor information fusion which was originated in the …

The concept of dependency in a neighborhood rough set model is an important evaluation
function for the feature selection. This function considers only the classification information …

Attribute reduction is one of the most important applications of fuzzy rough sets in machine
learning and pattern recognition. Most existing methods employ the intersection operation of …

Incremental feature selection approaches can improve the efficiency of feature selection
used for dynamic datasets, which has attracted increasing research attention. Nevertheless …

Neighborhood rough sets are widely used as an effective tool to deal with numerical data.
However, most of the existing neighborhood granulation models cannot well describe the …

Neighborhood rough set based online streaming feature selection methods have aroused
wide concern in recent years and played a vital role in processing high-dimensional data …

Rough set theory has been used extensively in fields of complexity, cognitive sciences, and
artificial intelligence, especially in numerous fields such as expert systems, knowledge …

Feature selection effectively reduces the dimensionality of data. For feature selection, rough
set theory offers a systematic theoretical framework based on consistency measures, of …

Multicriteria classification refers to classify objects evaluated by a set of criteria to preference-
ordered decision classes. Dominance-based rough set approach has been successfully …

Ordinal classification is a common classification problem, which widely exists in multi-
attribute decision making problems. The dominance-based rough set approach (DRSA) is a …