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Missing Value Imputation Using ANN Optimized by Genetic Algorithm
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Author(s): Anjana Mishra (Department of IT, C.V.Raman College of Engineering, Mahura, India), Bighnaraj Naik (Department of Computer Application, Veer Surendra Sai University of Technology, Burla, India)and Suresh Kumar Srichandan (Department of IT, Veer Surendra Sai University of Technology, Burla, India)
Copyright: 2021
Pages: 15
Source title:
Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-8048-6.ch002
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Abstract
Missing value arises in almost all serious statistical analyses and creates numerous problems in processing data in databases. In real world applications, information may be missing due to instrumental errors, optional fields and non-response to some questions in surveys, data entry errors, etc. Most of the data mining techniques need analysis of complete data without any missing information and this induces researchers to develop efficient methods to handle them. It is one of the most important areas where research is being carried out for a long time in various domains. The objective of this article is to handle missing data, using an evolutionary (genetic) algorithm including some relatively simple methodologies that can often yield reasonable results. The proposed method uses genetic algorithm and multi-layer perceptron (MLP) for accurately predicting missing data with higher accuracy.
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