A survey on optimal features selection for large datasets using machine learning algorithms

Abstract

Feature selection is the method of reducing data dimension while doing predictive analysis. One major reason
is that machine learning follows the rule of “garbage in-garbage out” and that is why one needs to be very concerned
about the data that is being fed to the model. In this survey, we review work in machine learning on methods for handling
data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting
relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on
these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use
to compare different methods. We close with some challenges for future work in this area.