Machine Learning and Its Application: A Quick Guide for Beginners

Feature Engineering

Author(s): Indranath Chatterjee *

Pp: 256-289 (34)

DOI: 10.2174/9781681089409121010010

* (Excluding Mailing and Handling)


The chapter on feature selection techniques deals with most state-of-theart feature selection techniques, which are being used alongside machine learning algorithms. The feature selection is a crucial element for the better performance of any machine learning algorithm. This chapter covers majorly two types of feature selection algorithms, namely, filter-based and evolutionary-based. This chapter covers two kinds of filter-based approaches in the filter-based algorithms, namely, hypothetical testing, such as t-test, z-test, ANOVA and MANOVA, and correlationbased such as Pearson's correlation, Chi-square test, and Spearman's rank correlation. This chapter also explains various methods such as genetic algorithms, particle swarm optimization, and ant colony optimization in evolutionary algorithms. For each of the algorithms, this chapter describes it in detail and the optimized algorithm for performing the feature selection approach.

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