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 …

Granular computing, as a new and rapidly growing paradigm of information processing, has
attracted many researchers and practitioners. Granular computing is an umbrella term to …

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

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 …

Gene expression data classification is an important technology for cancer diagnosis in
bioinformatics and has been widely researched. Due to the large number of genes and the …

Classical rough set theory is considered a useful tool for dealing with the uncertainty of
categorical data. The major deficiency of this model is that the classical rough set model is …

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 …

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 …

Feature selection is viewed as an important preprocessing step for pattern recognition,
machine learning, and data mining. Neighborhood is one of the most important concepts in …