In real applications of data mining, machine learning and granular computing, measurement
errors, test costs and misclassification costs often occur. Furthermore, the test cost of a …

Feature selection plays an important role in pattern recognition and machine learning.
Confronted with high dimensional data in many data analysis tasks, feature selection …

Feature selection is an essential process in data mining applications since it reduces a
model's complexity. However, feature selection with various types of costs is still a new …

Neighborhood rough set model is considered as one of the effective granular computing
models in dealing with numerical data. This model is now widely discussed in feature …

Neighborhood rough set is an important model in feature selection. However, it only
determines the granularity of the neighborhood from a feature perspective, while ignoring …

Feature selection is a challenging problem in many areas such as pattern recognition,
machine learning and data mining. Rough set theory, as a valid soft computing tool to …

In real application domains, acquiring fine-grained data has a higher cost than coarse-
grained data. To achieve the best results at the lowest cost, it is necessary to select an …

Feature subset selection presents a common challenge for the applications where data with
tens or hundreds of features are available. Existing feature selection algorithms are mainly …

Feature selection is a vital preprocessing step in real applications of data mining and
machine learning. With the prevalence of high-dimensional hybrid data sets in real-world …

Feature selection is an important preprocessing step in machine learning and data mining.
In real-world applications, costs, including money, time and other resources, are required to …