计算机科学 ›› 2017, Vol. 44 ›› Issue (6): 189-198.doi: 10.11896/j.issn.1002-137X.2017.06.032
孙强,魏伟,侯培鑫,岳继光
SUN Qiang, WEI Wei, HOU Pei-xin and YUE Ji-guang
摘要： 异常检测是系统运行维护的重要工作。在系统运行过程中可获得大量正常的运行数据,但异常数据的获取成本较高,因此可引入单分类器的思想来处理异常检测问题。测量不确定性、环境噪声、存储设备等导致监测数据可能存在不确定性。利用区间数描述不确定的监测数据,提出区间数样本的核可能性1-均值单簇聚类-单分类器异常检测算法。分别考虑聚类中心位于输入空间与特征空间两种情况,并考虑区间数样本具有的区间宽度不均衡性,提出区间细分检测策略。结合人工数据集与UCI数据集给出的算例验证了所提算法的有效性,其与现有SVM-OCC相比具有更高性能。
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