Feature selection goal is to get rid of redundant and irrelevant features. The problem of
feature subset selection is that of finding a subset of the original features of a dataset, such …

Data mining methods are frequently applied in the framework of data classification. Under
data mining methods, feature selection (FS) algorithms are essential for dealing with various …

Feature selection methods are deployed in machine-learning algorithms for reducing the
redundancy in the dataset and to increase the clarity in the system models without loss of …

Feature Selection (FS) is an imperative issue in data mining and machine learning. It is an
inevitable task to shorter the number of features presented in the initial data set for better …

Feature selection has become interest to many research areas which deal with machine
learning and data mining, because it provides the classifiers to be fast, cost-effective, and …

Sažetak Aiming to deal with the irrelevant or redundant features, this paper proposes eight
kinds of feature selection methods. The first seven feature selection methods include CART …

Background/Objectives: A novel feature selection framework using minimum variance
method is proposed. The purpose of the proposed method is to reduce the computational …

Classification of data crosses different domains has been extensively researched and is one
of the basic methods for distinguishing one from another, as we need to know which belongs …

Feature selection is the process of eliminating features from the data set that are irrelevant
with respect to the task to be performed. Feature selection is important for many reasons …

Objectives: This study summarizes the feature selection process, its importance, different
types of feature selection algorithms such as Filter, Wrapper and Hybrid. Moreover, it …