A survey on optimal features selection for large datasets using machine learning algorithms
| Author(s) | : | Mrs.E.Aarthi, Dr.P.Muthulakshmi |
| Institution | : | Department of Computer Science, SRMIST, Chennai, India |
| Published In | : | Vol. 6, Issue 7 — July 2019 |
| Page No. | : | 82-89 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Feature selection is the method of reducing data dimension while doing predictive analysis. One major reasonis that machine learning follows the rule of “garbage in-garbage out” and that is why one needs to be very concernedabout the data that is being fed to the model. In this survey, we review work in machine learning on methods for handlingdata sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selectingrelevant features, and the problem of selecting relevant examples. We describe the advances that have been made onthese topics in both empirical and theoretical work in machine learning, and we present a general framework that we useto compare different methods. We close with some challenges for future work in this area.
Mrs.E.Aarthi, Dr.P.Muthulakshmi, “A survey on optimal features selection for large datasets using machine learning algorithms”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 6, Issue 7, pp. 82-89, July 2019.
