Analytical Approach of the Allegation of Nonexistent and Deficient Knowledge Using Machine Learning Techniques
| Author(s) | : | S.kanchana |
| Institution | : | Assistant Professor, Faculty of Science and Humanities SRM Institute of Science & Technology, Chennai, Tamilnadu, India |
| Published In | : | Vol. 5, Issue 2 — February 2018 |
| Page No. | : | 582-588 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Missing data is a problem that infuses most important issue faced by researchers and practitioners who useindustrial and research databases is incompleteness of data, usually in terms of missing or erroneous values. Some of thedata analysis algorithms can work with incomplete data, a large portion of work require complete data. Therefore, variety ofmachine learning (ML) techniques are developed to reprocess the incomplete data. This paper concentrates on differentimputation techniques and also proposes supervised and unsupervised machine learning techniques Naïve Bayesianimputation method in MI model. The analysis is carried out using a comprehensive range of databases, for which missingvalues were introduced randomly. The goal of this paper is to provide general guidelines on selection of suitable dataimputation algorithms based on characteristics of the data.
S.kanchana, “Analytical Approach of the Allegation of Nonexistent and Deficient Knowledge Using Machine Learning Techniques”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 2, pp. 582-588, February 2018.
