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AL-Rafidain Journal of Computer Sciences and Mathematics

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Improved Classification Performance of Support Vector Machine Technique Using the Genetic Algorithm

    Omar Saber Qasim Mustafa Ayham Abed Alhafedh

AL-Rafidain Journal of Computer Sciences and Mathematics, 2018, Volume 12, Issue 2, Pages 49-60
10.33899/csmj.2018.163581

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Abstract

In this research, the genetic algorithm was proposed as a method to find the parameters of support vector machine, specifically the σ and c parameters for kernel and the hyperplane respectively. Based on the Least squares method, the fitness function was built in the genetic algorithm to find the optimal values of the parameters in the proposed method. The proposed method showed better and more efficient results than the classical method of support vector machine which adopts the default or random values of parameters σ and c in the classification of leukemia data.
 
Keywords:
    Genetic algorithm Support Vector Machine Parameter Selection
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(2018). Improved Classification Performance of Support Vector Machine Technique Using the Genetic Algorithm. AL-Rafidain Journal of Computer Sciences and Mathematics, 12(2), 49-60. doi: 10.33899/csmj.2018.163581
Omar Saber Qasim; Mustafa Ayham Abed Alhafedh. "Improved Classification Performance of Support Vector Machine Technique Using the Genetic Algorithm". AL-Rafidain Journal of Computer Sciences and Mathematics, 12, 2, 2018, 49-60. doi: 10.33899/csmj.2018.163581
(2018). 'Improved Classification Performance of Support Vector Machine Technique Using the Genetic Algorithm', AL-Rafidain Journal of Computer Sciences and Mathematics, 12(2), pp. 49-60. doi: 10.33899/csmj.2018.163581
Improved Classification Performance of Support Vector Machine Technique Using the Genetic Algorithm. AL-Rafidain Journal of Computer Sciences and Mathematics, 2018; 12(2): 49-60. doi: 10.33899/csmj.2018.163581
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