Feature selection in the data with different types of feature values, ie, the heterogeneous or
mixed data, is especially of practical importance because such types of data sets widely …

Rough set theory has been extensively discussed in machine learning and pattern
recognition. It provides us another important theoretical tool for feature selection. In this …

Multi-label learning deals with data associated with a set of labels simultaneously. Like
traditional single-label learning, the high-dimensionality of data is a stumbling block for multi …

Feature selection as a combinatorial optimization problem is an important preprocessing
step in data mining; which improves the performance of the learning algorithms with the help …

Recently, by combining rough set theory with granular computing, pessimistic and optimistic
multigranulation rough sets have been proposed to derive “AND” and “OR” decision rules …

Identifying fault categories, especially for compound faults, is a challenging task in
mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method …

As an extension of Pawlak rough set model, decision-theoretic rough set model (DTRS)
adopts the Bayesian decision theory to compute the required thresholds in probabilistic …

Multi-label classification is an active research field in machine learning. Because of the high
dimensionality of multi-label data, attribute reduction (also known as feature selection) is …

Attribute reduction with rough sets is an effective technique for obtaining a compact and
informative attribute set from a given dataset. However, traditional algorithms have no …

Neighborhood rough set model is considered as one of the effective granular computing
models in dealing with numerical data. This model is now widely discussed in feature …