计算机科学 ›› 2018, Vol. 45 ›› Issue (10): 155-159.doi: 10.11896／j.issn.1002-137X.2018.10.029
徐东, 王岩俊, 孟宇龙, 张子迎
XU Dong, WANG Yan-jun, MENG Yu-long, ZHANG Zi-ying
摘要： 针对现有的基于隔离森林(Isolation Forest)的数据异常检测算法检测精度低、执行效率差和泛化能力弱等问题,提出一种改进的数据异常检测方法SA-iForest。该方法基于模拟退火算法选择精度高和有差异性的隔离树来优化森林,同时去除冗余的隔离树,改进了隔离森林的森林构建。采用标准仿真数据集对所提方法进行验证,结果表明该方法与传统Isolation Forest和LOF方法相比,在准确率、执行效率和稳定性方面均有显著提高。
中图分类号:
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