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 …

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 …

对于空间中的任一子集, 通过基本邻域信息粒子进行逼近, 由此提出了邻域信息系统和邻域决策
表模型. 分析了该模型的性质, 并且基于此模型构造了数值型属性的选择算法. 利用UCI …

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

Feature subset selection is viewed as an important preprocessing step for pattern
recognition, machine learning and data mining. Most of researches are focused on dealing …

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 …

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 …

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