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 …

Feature selection is a challenging problem in areas such as pattern recognition, machine
learning and data mining. Considering a consistency measure introduced in rough set …

A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy
rough dependency as a criterion for feature selection. However, this model can merely …

Many real data increase dynamically in size. This phenomenon occurs in several fields
including economics, population studies, and medical research. As an effective and efficient …

Fuzzy rough set theory is a powerful tool to deal with uncertainty information, which has
been successfully applied to the fields of attribute reduction, rule extraction, classification …

Unsupervised attribute reduction becomes very challenging due to a lack of decision
information, which is to select a subset of attributes that can maintain learning ability without …

With the advent of the age of big data, a typical big data set called limited labeled big data
appears. It includes a small amount of labeled data and a large amount of unlabeled data …

As a supervised learning method, classical rough set theory often requires a large amount of
labeled data, in which concept approximation and attribute reduction are two key issues …

In real-applications, there may exist many kinds of data (eg, boolean, categorical, real-
valued and set-valued data) and missing data in an information system which is called as a …