The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …

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 …

Neighborhood rough set is a useful mathematic tool to describe uncertainty in mixed data.
Feature selection based on neighborhood rough set has been studied widely. However …

Existing feature selection methods easily neglect the distribution of data, and require most of
the neighborhood radius in neighborhood rough sets (NRS) to be selected artificially. These …

The principle of any approach for solving feature selection problem is to find a subset of the
original features. Since finding a minimal subset of the features is an NP-hard problem, it is …

Feature selection has aroused extensive attention and aims at selecting features that are
highly relevant to classification from raw datasets to improve the performance of a learning …

In this paper, we introduced a novel feature selection method based on the hybrid model
(filter-wrapper). We developed a feature selection method using the mutual information …

Neighborhood rough set has been proven to be an effective tool for feature selection. In this
model, the positive region of decision is used to evaluate the classification ability of a subset …

Dominance-based neighborhood rough set (DNRS) is capable to give qualitative and
quantitative descriptions of the relations between ordered objects. In spite of its effectiveness …

Rough set theory is one of the effective methods to feature selection which can preserve the
characteristics of the original features by deleting redundant information. The main idea of …