Similar to feature selection over completely labeled data, the aim of feature selection over
partially labeled data (semi-supervised feature selection) is also to find a feature subset …

Feature selection is an important preprocessing step in machine learning and data mining,
and feature criterion arises a key issue in the construction of feature selection algorithms …

The optimal global feature subset cannot be found easily due to the high cost, and most
swarm intelligence optimization-based feature selection methods are inefficient in handling …

In neighborhood rough set, radius is a key factor. Different radii may generate different
neighborhood relations for discriminating samples. Unfortunately, it is possible that two …

In fields such as pattern recognition and computational intelligence, attribute subset
selection has gradually attracted the attention of researchers as a challenging issue …

Feature selection problem in data mining is addressed here by proposing a bi-objective
genetic algorithm based feature selection method. Boundary region analysis of rough set …

Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …

Feature selection is an important preprocessing step for high-dimensional data mining and
machine learning; it is viewed as the selection of the optimal granularity to describe the …

Attribute reduction is an important problem in data mining and machine learning in that it can
highlight favorable features and decrease the risk of over-fitting to improve the learning …

Nowadays the incredibly advanced developments in information technologies have led to
exponential growth in the datasets with respect to both the dimensionality and the sample …