TY - JOUR
AR - COCB-2019-1-103
TI - Classification of Diseases Using a Hybrid Binary Bat Algorithm with Mutual Information Technique
AU - Firas Ahmed, Yonis
AU - Omar Saber, Qasim
JO - Clinical Oncology and Cancer Biology
PY - 2019
DA - Sat 28, Dec 2019
SN - 2733-2276
DO - http://dx.doi.org/10.31487/j.COCB.2019.01.03
UR - https://www.sciencerepository.org/classification-of-diseases-using-a-hybrid-binary-bat-algorithm_COCB-2019-1-103 
KW - Binary bat algorithm (BBA), classification, mutual information, bigdata
AB - 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