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World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

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Indexing

Abstract

AN EMPIRICAL APPROACH OF THE ACCUSATION OF UNREAL AND INCOMPLETE DATA USING LEARNING TECHNIQUES

S. Kanchana* and Dr. Antony Selvadoss Thanamani

ABSTRACT

Unreal and Incomplete data is a problem that focuses most important issue faced by researchers and practitioners who use industrial and research databases is incompleteness of data, usually in terms of missing or erroneous values. More or less of the data analysis algorithms can operate with incomplete data, a big share of the work require complete data. Therefore, variety of machine learning (ML) techniques are developed to reprocess the incomplete information. This report centres on different imputation techniques and also proposes a supervised and unsupervised machine learning techniques Naïve Bayesian imputation method in MI model. The analysis is carried out employing a comprehensive range of databases, for which missing values were presented randomly. The goal of this report is to offer general guidelines for selection of suitable data imputation algorithms based on characteristics of the data.

[Full Text Article]