计算机科学 ›› 2020, Vol. 47 ›› Issue (1): 110-116.doi: 10.11896/jsjkx.181001921
金耀1,徐丽亚1,吕慧琳1,顾苏杭2,3
JIN Yao1,XU Li-ya1,LV Hui-lin1,GU Su-hang2,3
摘要: 真实数据集中存在的对抗样本易导致分类器取得较差的分类性能,但如果其能够被合理利用,分类器的泛化能力将得到显著提高。针对现有大部分分类器并没有涉及对抗样本信息的问题,提出一种攻击标签信息的堆栈式支持向量机。该方法从给定的初始数据集中选取一定比例的样本,并攻击所选取样本的标签,使之成为对抗样本,即将样本标签替换成其他不同类型的标签,利用支持向量机训练包含对抗样本的数据集,从而生成对抗支持向量机。计算对抗支持向量机的输出误差相对于输入样本的一阶梯度信息,并将其嵌入到输入样本特征中以更新输入样本。将更新后的样本输入到下一个对抗支持向量机中,并重新训练。以堆栈方式级联一定数目的对抗支持向量机,直至取得最好的分类性能。原理分析与实验结果表明,基于对抗样本的一阶梯度信息不仅提供了分类器输出与输入之间的一种正相关关系,而且为堆栈式支持向量机中的子分类器提供了一种新的堆栈方式,并提高了分类器的整体性能。
中图分类号:
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