As a combinatorial optimization problem, feature selection has been widely used in machine
learning and data mining. In this paper, a feature selection method using forest optimization …

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

Filter-based feature selection techniques are less complex compare to Wrapper-based
feature selection techniques in case of High Dimensional datasets. In this paper, we …

Naive Bayes (NB) classifier is a simple and efficient classifier, but the independent
assumption of its attribute limits the application of the actual data. This paper presents an …

Variable and feature selection have become the focus of much research in areas of
application for which datasets with tens or hundreds of thousands of variables are available …

The purpose of this study is to evaluate the most important features of graduate
employability in higher education database, in attempt to measure the employability …

Feature selection is a robust technique for data reduction and an essential step in successful
machine learning applications. Different feature selection methods have been introduced in …

Some features in a dataset that contain irrelevant or unnecessary data may adversely affect
both classification accuracy and the size of data. These negative effects are minimized by …

This paper focuses on improving the classification accuracy for supervised learning in areas
of application with very few training data and with extremely available high dimensionality …