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 …

A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of
decision system and it has been applied successfully in all the fields. It is used to identify the …

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 …

Classification accuracy highly dependents on the nature of the features in a dataset which
may contain irrelevant or redundant data. The main aim of feature selection is to eliminate …

Hybrid metaheuristics are of the most interesting recent trends in optimization and memetic
algorithms. In this paper, two hybridization models are used to design different feature …

Feature Selection (FS) is a challenging machine learning-related task that aims at reducing
the number of features by removing irrelevant, redundant and noisy data while maintaining …

In this paper, binary variants of the Butterfly Optimization Algorithm (BOA) are proposed and
used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA …

Searching for the optimal subset of features is known as a challenging problem in feature
selection process. To deal with the difficulties involved in this problem, a robust and reliable …

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 …

Wrapper feature selection methods aim to reduce the number of features from the original
feature set to and improve the classification accuracy simultaneously. In this paper, a …