Survey on Feature Selection

TL;DR

This survey reviews various feature selection methods, emphasizing their importance in reducing computational load and improving machine learning accuracy across different algorithms.

Contribution

It provides a comprehensive overview of feature selection approaches and their relationships with machine learning algorithms, highlighting their roles and benefits.

Findings

Feature selection reduces computational burden.

Feature selection improves machine learning accuracy.

Different approaches are suited for different algorithms.

Abstract

Feature selection plays an important role in the data mining process. It is needed to deal with the excessive number of features, which can become a computational burden on the learning algorithms. It is also necessary, even when computational resources are not scarce, since it improves the accuracy of the machine learning tasks, as we will see in the upcoming sections. In this review, we discuss the different feature selection approaches, and the relation between them and the various machine learning algorithms.