Characteristics extracted from the training datasets of classification problems have proven to
be effective predictors in a number of meta-analyses. Among them, measures of …

Feature selection aims to remove irrelevant or redundant features and thereby remain
relevant or informative features so that it is often preferred for alleviating the dimensionality …

Neighborhood rough sets based attribute reduction, as a common dimension reduction
method, has been widely used in machine learning and data mining. Each attribute has the …

Attribute reduction is one of the biggest challenges encountered in computational
intelligence, data mining, pattern recognition, and machine learning. Effective in feature …

Multi-label learning deals with data belonging to different labels simultaneously. Like
traditional supervised feature selection, multi-label feature selection also plays an important …

Attribute reduction is one of the most important preprocessing steps in machine learning and
data mining. As a key step of attribute reduction, attribute evaluation directly affects …

The scale of the radius for constructing neighborhood relation has a great effect on the
results of neighborhood rough sets and corresponding measures. A very small radius …

The ontology-based information systems (IS) development is beneficial for analyzing,
conceptual modeling, designing, and re-engineering complex IS to be semantically enriched …

The feature selection has been widely used to reduce the data dimensionality. Data
reduction improve the classification performance, the approximation function, and pattern …

Recently, a multigranulation rough set (MGRS) has become a new direction in rough set
theory, which is based on multiple binary relations on the universe. However, it is worth …