The evolutionary algorithms (EAs) have been shown favorable performance for feature
selection. However, a large number of evaluations are required through the EAs. Thus, they …

Feature selection is an effective approach to reduce the number of features of data, which
enhances the performance of classification in machine learning. In this paper, we formulate …

In recent years, feature selection research has quickly advanced to keep up with the age of
developing expert systems. This is because the applications of these systems sometimes …

Feature selection (FS) is an important preprocessing technique for dimensionality reduction
in classification problems. Particle swarm optimization (PSO) algorithms have been widely …

In the feature selection process, reaching the best subset of features is considered a difficult
task. To deal with the complexity associated with this problem, a sophisticated and robust …

Feature selection is a task of choosing the best combination of potential features that best
describes the target concept during a classification process. However, selecting such …

Feature selection is an integral part of any machine learning system and the success of such
systems highly depends on the relevance of features with the target domain. Feature …

Classification data are usually represented by many features, but not all of them are useful.
Without domain knowledge, it is challenging to determine which features are useful. Feature …

Feature selection (FS) is an essential component of data mining and machine learning. Most
researchers devoted to get more effective method with high accuracy and fewer features, it …

In this paper, a feature selection approach that based on Binary Particle Swarm Optimization
(PSO) with time varying inertia weight strategies is proposed. Feature Selection is an …