Open AccessGenetic Algorithm as a Key Parameter of SVM parameter optimization and feature selection for acute Leukemia diagnosis
Author(s) -
Najaat Ahmed Abdullah,
Mohammed Abduljalil Ibrahim,
Adel Sallam Haider
Publication year - 2020
Publication title -
mağallaẗ ğāmi'aẗ 'adan li-l-'ulūm al-ṭabīyyaẗ wa-al-taṭbīqiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2788-9327
pISSN - 1606-8947
DOI - 10.47372/uajnas.2020.n2.a07
Subject(s) - support vector machine , kernel (algebra) , selection (genetic algorithm) , genetic algorithm , computer science , feature selection , artificial intelligence , machine learning , pattern recognition (psychology) , key (lock) , kernel method , algorithm , mathematics , combinatorics , computer security
The selection process of the kernel parameters and the relevant features are very crucial to enhance the classification tasks. Thus, in this work, a genetic algorithm that mimics the biological evaluation is used to optimize the support vector machine kernel parameters in order to achieve a high classification accuracy of an acute leukemia diagnosis. The results proved that the combination of genetic algorithm with support vector machine increased the classification accuracy of acute leukemia diagnosis to 99.19%, compared with the value of 89.43% obtained under default support vector machine kernel parameters. This can be directly attributed to the elimination of the irrelevant features and the suitable selection of the kernel parameters. This implies that the genetic algorithm model can be adequately used to solve the optimization problem and features subset selection that gives the optimal accuracy.