In this paper a hybrid feature selection method is proposed which takes advantages of
wrapper subset evaluation with a lower cost and improves the performance of a group of …

Feature selection algorithms are the cornerstone of machine learning. By increasing the
properties of the samples and samples, the feature selection algorithm selects the significant …

Feature selection aims to choose an optimal subset of features that are necessary and
sufficient to improve the generalization performance and the running efficiency of the …

This paper considers feature selection as a problem of an aggregation of three state-of-the-
art filtration methods: Pearson's linear correlation coefficient, the ReliefF algorithm and …

Curse dimensionality is the most problem in features fusion, data mining and pattern
recognition. To solve this problem there are two methods: Feature Reduction and Feature …

Feature subset selection is of immense importance in the field of data mining. The increased
dimensionality of data makes testing and training of general classification method difficult …

In this paper different classification techniques of Data Mining are compared using diverse
datasets from University of California, Irvine (UCI). Accuracy and time required for execution …

Feature Subset Selection is an essential pre-processing task in Data Mining. Feature
selection process refers to choosing subset of attributes from the set of original attributes …

In recent years, Jaya optimization algorithm has been successfully applied in several
optimization problems. This paper presents a novel feature selection (FS) approach based …