[author_cn_name].[cn_title][J].空军工程大学学报:自然科学版,[year_id],[volume]([issue]):[start_page]-[end_page] 低阶数据映射和特征加权的线性SVM-Low Degree Data Mappings and Feature Weighted via Linear SVM
文章摘要
王瑞,向新,肖冰松.低阶数据映射和特征加权的线性SVM[J].空军工程大学学报:自然科学版,2019,20(4):72-77
低阶数据映射和特征加权的线性SVM
Low Degree Data Mappings and Feature Weighted via Linear SVM
  
DOI:
中文关键词: 线性支持向量机  核支持向量机  低阶多项式映射  隐性信息  特征加权  模糊熵
英文关键词: linear svm  kernel svm  low degree polynomial mappings  hidden information  feature weighted  fuzzy entropy
基金项目:
作者单位
王瑞,向新,肖冰松 空军工程大学航空工程学院,西安,710038 
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中文摘要:
      针对传统线性支持向量机在训练数据集时均等对待每一维输入特征,以及在原始空间直接分类造成预测准确率低的问题,提出低阶多项式数据映射和特征加权相结合的方法,来提高线性支持向量机的分类性能。该方法首先将每个样本映射到多项式核对应的2 阶显式特征空间,从而增加样本的隐性信息,然后使用模糊熵特征加权算法计算每一维特征的权重,通过权重衡量特征对分类结果的贡献大小。从不同数据库选取7个数据集进行测试,在训练时间和预测准确率2个方面将该方法与核支持向量机、线性支持向量机的其他改进算法进行比较。结果显示,随着数据集规模的扩大,训练时间降低一个数量级,预测准确率在一些数据集上取得与核支持向量机相接近的效果。结果表明:所提方法可以有效提高线性支持向量机的整体性能。
英文摘要:
      Aimed at the problems that he traditional linear Support Vector Machine (SVM) is equal to each dimension of input features in training data sets, and the classification in the original space directly leads to the low prediction accuracy, a method for combining low degree polynomial mappings and feature weighted is proposed to improve the classification performance of linear SVM. First of all, each sample is mapped into the two degree explicit feature space by using the polynomial trick to increase the implicit information of samples. Then, the feature weights of each dimension are calculated by using the fuzzy entropy feature weighted algorithm. By feature weighting, the magnitude of contribution to the result of classification can be measured. To verify the robust of the proposed method, the totally seven data sets from different database are tested. Making a comparison between Kernel SVM and other improved Linear SVM algorithms in training time and prediction accuracy, the results show that the training time reduces an order of magnitude with the expansion of data set, and the prediction accuracy can keep up with few training samples even close to Kernel SVM. The running results show that the proposed method can effectively improve the overall performance of the linear vector machine.
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