计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210800216-5.doi: 10.11896/jsjkx.210800216
戴小路, 汪廷华, 周慧颖
DAI Xiao-lu, WANG Ting-hua, ZHOU Hui-ying
摘要： 模糊支持向量机通过引入模糊隶属度有效区分不同样本的重要程度,降低了传统支持向量机对噪声数据的敏感性。针对基于欧氏距离设计的隶属度函数忽略了样本的总体分布,且未考虑样本特征重要性的区分,提出了一种基于加权马氏距离的模糊支持向量机方法。首先应用Relief-F算法计算样本特征权重,然后基于该权重计算样本距其类中心的加权马氏距离,最后根据该距离值度量样本隶属度。在此基础上,考虑到核函数及其核参数难以确定,将模糊支持向量机与多核学习方法相结合,提出基于加权马氏距离的模糊多核支持向量机,采用加权求和形式构建多核,并遵循中心核对齐原则确定每个核的权重。该方法不仅降低了弱相关特征对分类效果的影响,而且使数据表达更加全面准确。实验结果表明,基于加权马氏距离的模糊支持向量机的分类精度高于基于欧氏距离和基于马氏距离的模糊支持向量机,且基于加权马氏距离的模糊多核支持向量机的分类性能较单核模型更优。
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