In recent years, feature selection for multilabel classification has attracted attention in
machine learning and data mining. However, some feature selection methods ignore the …

The existing three-way decision (TWD)-making methods cannot effectively handle
incomplete multiattribute decision-making (MADM) problems in real life; it is necessary to …

In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …

For heterogeneous data sets containing numerical and symbolic feature values, feature
selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very …

Feature selection based on neighborhood rough set is a noteworthy step in dealing with
numerical data. Information entropy, proven in many theoretical analysis and practical …

The existing multiattribute decision-making (MADM) methods on multiscale information
systems (MSISs) are generally studied from the utility point of view, which may cause two …

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 …

Recently, the advancement of cognitive computing and three-way decisions has enabled in-
depth sequential pattern understanding through temporal association analysis. The main …

As a complicated cognitive process, multi-attribute decision-making usually focuses on the
decision-making issue of seeking the optimal alternative or ranking alternatives under the …

Pawlak conflict analysis focuses on three-valued ratings of a set of agents on a set of issues,
in which the three values+, 0, and− indicate, respectively, that an agent is positive, neutral …