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DOI: 10.14569/IJACSA.2020.0110288
PDF

Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

Author 1: Aysh Alhroob
Author 2: Wael Alzyadat
Author 3: Ikhlas Almukahel
Author 4: Hassan Altarawneh

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 2, 2020.

  • Abstract and Keywords
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Abstract: Data exists in large volume in the modern world, it becomes very useful when decoded correctly to inform decision making towards tackling real word issues. However, when the data is conflicting, it becomes a daunting task to get obtain information. Working on missing data has become a very impor-tant task in big data analysis. This paper considers the handling of the missing data using the Support Vector Machine (SVM) based on a technique called Correlation-Genetic Algorithm-SVM. This data is to be subjected to the SVM classification technique after identifying the attribute’s correlation and application of the genetic algorithm. The application of the correlation enables a clear view of the attributes which are highly correlated within a particular dataset. The results indicate that apart from the SVM, the application of the proposed hybrid algorithm produces better outcomes identification rate and accuracy is considered. The proposed approach is also compared with depicts the Mean Identification rate of applying the neural network, the result indicate a consistent accuracy hence making it better.

Keywords: Missing data; Support Vector Machine (SVM); ge-netic algorithm; hybrid algorithm; correlation

Aysh Alhroob, Wael Alzyadat, Ikhlas Almukahel and Hassan Altarawneh, “Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 11(2), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110288

@article{Alhroob2020,
title = {Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110288},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110288},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {2},
author = {Aysh Alhroob and Wael Alzyadat and Ikhlas Almukahel and Hassan Altarawneh}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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