Classification of Diseases Using a Hybrid Binary Bat Algorithm with Mutual Information Technique

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Author Info

Firas Ahmed Yonis Omar Saber Qasim

Corresponding Author

Firas Ahmed Yonis
Department of Mathematics, University of Mosul, Mosul, Iraq

A B S T R A C T

Many features change the results of calculations and change this may be a negative impact on the accuracy of the results, especially if the data used is large. Evolutionary algorithms are used to find the fastest and best way to perform these calculations, such as the bat algorithm (BA) by reducing the dimensions of the search area after changing it from continuous to discrete. In this paper, we will propose a method of linking and conclude the selection of the best and most influential features on the results by neglecting the negative impact features through three stages: the first will be the arrangement of the features columns according to their importance Starting of the most important using mutual information technology and the second stage the process of cutting these features into A certain limit and content with the most important and the calculations using the workbook NAVI_BAIS and then the final stage using the bat algorithm (BBA). The proposed algorithm describes speed, efficiency, and accuracy so that it produces high-precision results based on fewer features

Article Info

Article Type

Research Article

Publication history

Received: Mon 25, Nov 2019
Accepted: Wed 18, Dec 2019
Published: Sat 28, Dec 2019

Copyright

© 2023 Firas Ahmed Yonis. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Hosting by Science Repository.
DOI: 10.31487/j.COCB.2019.01.03