文章基本信息

标题：Evolving Self-Adaptive Genetic Algorithm in Nonlinear Support Vector Machines for Classification Problems
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作者：Mohammad Mezher ; Maysam Abbod
期刊名称：Annals. Computer Science Series
印刷版ISSN：1583-7165
电子版ISSN：2065-7471
出版年度：2010
卷号：8
期号：2
页码：99-112
出版社：Mirton Publishing House, Timisoara
摘要：Support Vector Machines (SVM) has shown a range of promising applications on classification problems. In this paper, we propose the genetic algorithm that employs Self-Adaptive Mutation Rate (SAMR) to develop kernel functions for SVM classifiers. The proposed SAMR model implemented the hybrid model for three advanced non-linear classification algorithms and shows competitive results in comparing to Grid SVM. Five publicly available datasets, cross validation correctness for Area Under Curve (AUC) have been involved. Improvements achieved may lead to biomarkers results