Abstract: Orthogonal design can find out the optimal combination of factors with fewer experiments.Besides dealing with small samples，Support Vector Machine（SVM） has good generalization ability and is immune to the restriction of the data dimension.In this paper，a method using SVM and orthogonal design is proposed，in which training and testing are arranged according to the feature numbers of datasets and the structure of orthogonal table，and finally，experiments are carried out on the selected subsets of features.The results indicate that the proposed method can not only discard the redundant features but also gain better classification accuracy than that on the datasets with full features.

摘要： 正交设计利用较少的实验次数就可以找出因素间的最优搭配，支持向量机能处理小样本、具有很好的泛化能力且不受数据集维数的制约。结合二者的优势，提出了基于支持向量机和正交设计的特征选择方法，根据数据集的特征数目及相应正交表的结构，安排训练、测试，最后对优选出的特征子集检验，实验结果表明该特征选择方法能够去除冗余特征而且能取得比使用特征全集更高的分类率。