计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 144-151.doi: 10.11896/jsjkx.210100142
武玉坤, 李伟, 倪敏雅, 许志骋
WU Yu-kun, LI Wei, NI Min-ya, XU Zhi-cheng
摘要： 大规模高维不平衡数据是异常检测中的重大挑战。单类支持向量机在处理不平衡数据方面非常有效,但不适合大规模高维数据,同时单类支持向量机的核函数对检测性能也具有重要的影响。文中提出了一个深度自编码器与单类支持向量机相结合的异常检测模型,深度自编码器不仅负责提取特征和降维,同时拟合出了一个自适应核函数。深度自编码器与单类支持向量机共享损失函数,实现了端到端的训练。作为一个整体,模型采用梯度下降法进行联合训练。在4个公开数据集上与其他异常检测方法进行了对比实验。实验结果表明,在AUC以及召回率(RECALL)方面,所提模型的性能优于单核和多核单类支持向量机以及其他模型,并且所提模型在不同异常率时是鲁棒的,在时间复杂度方面也具有非常大的优势。
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