计算机科学 ›› 2024, Vol. 51 ›› Issue (4): 359-365.doi: 10.11896/jsjkx.230500034
邢开颜, 陈文
XING Kaiyan, CHEN Wen
摘要： 当前正负类训练样本分布不均衡的问题已极大地限制了离群检测模型的性能。基于主动学习的离群点检测算法能够通过对样本分布的主动学习,自动合成离群点以平衡训练数据分布。然而,传统的基于主动学习的检测方法缺乏对合成离群点的质量评估和过滤筛选,导致通过主动学习过程合成的训练样本点中存在样本噪声,并降低了分类模型的性能。针对上述问题,提出了基于反向标签传播的多生成器主动学习算法(Multi-Generator Active Learning Algorithm Based on Reverse Label Propagation,MG-RLP),其包括多个神经网络生成器和一个用于离群点边界检测的鉴别器。MG-RLP通过多个子生成器生成多分布特征的样本数据,以防止单生成器合成的训练样本过于聚集而导致的模式崩塌问题。同时,MG-RLP利用反向标签传播过程对神经网络生成的样本点进行质量评估,以筛选出可信的合成样本。筛选后的样本被保留在训练样本中用于对鉴别器进行迭代训练,以提升对离群点的检测性能。基于5个公共数据集,对比验证了MG-RLP与6种典型的离群点检测算法的性能,结果表明,MG-RLP在AUC和检测精度指标上分别提高了15%和22%,结果验证了MG-RLP的有效性。
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