Univariate and Multivariate Filtering Techniques for Feature Selection and Their Applications in Field of Machine Learning

Abstract

Machine learning's feature selection technique aids in the selection of a subset of original features in order to decrease high-dimensional data space. As per the literature, there are two basic strategies for feature selection: supervised and unsupervised. This chapter will focus on supervised filtering approaches only. Filter, intrinsic, and wrapper are the three types of supervised filtering algorithms. Filtering strategies are the subject of this chapter. The chapter covers the most popular univariate filtering algorithms with examples, advantages and downsides, and R implementation. The chapter compares univariate filtering techniques with number of parameters. The chapter also depicts two popular multivariate filtering techniques: minimum redundancy and maximum relevance (mRMR) and correlation-based feature selection (CFS) using appropriate example and implementation with R programming. Finally, the chapter deals with prominent applications of filtering techniques in context to machine learning.