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 has been widely applied in many areas such as classification of spam
emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray …

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 …

Classification is a very vital task that is performed in machine learning. A technique used for
classification is trained on various instances to foresee the class labels of hidden instances …

Datasets often include excessive or irrelevant data that affect the performance and
complexity of the machine learning model. Feature selection is one of the most effective …

Prediction plays a vital role in decision making. Correct prediction leads to right decision
making to save the life, energy, efforts, money and time. The right decision prevents physical …

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

Data mining is an inevitable step in knowledge discovery and it helps discovering hidden
and useful patterns among data. These days, the number of stored attributes for each entity …

Commonly, when a feature selection algorithm is applied, a single feature subset is selected
for all the classes, but this subset could be inadequate for some classes. Class-specific …

With the rapid development of information techniques, the dimensionality of data in many
application domains, such as text categorization and bioinformatics, is getting higher and …