文章基本信息

标题：Application of Improved Grid Search Algorithm on SVM for Classification of Tumor Gene
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作者：Li Wenwen ; Xing Xiaoxue ; Liu Fu 等
期刊名称：International Journal of Multimedia and Ubiquitous Engineering
印刷版ISSN：1975-0080
出版年度：2014
卷号：9
期号：11
页码：181-188
DOI：10.14257/ijmue.2014.9.11.18
出版社：SERSC
摘要：According to the merits and shortcomings of the traditional gridsearch algorithm in parameters optimization of support vector machine (SVM), an improved grid search algorithm is proposed. Dichotomous search algorithm is used to reduce target searching range. First, searching range is determined roughly, and a set of parameters are obtained. Then fine search is applied in reduction the range for searching, and searching the optimum parameters.Three kinds of famous tumor gene data set are used in the comparison experiments to validate the classification accuracy of principal component analysis (PCA)- SVM and kernel principal component analysis (KPCA)-SVM. Experiment results and data analysis shows that, comparing with traditional gridsearch algorithm, the proposed method has higher classification accuracy and less search time.
关键词：Artificial intelligence; Gridsearch; Support vector machine; Tumor gene data ; set; Principal component analysis