信息与控制2016，Vol.45Issue(4)：471-478,8.DOI:10.13976/j.cnki.xk.2016.0471
一种基于多示例学习的局部离群点检测算法
Local Outlier Detection Algorithm Based on Multi-instance Learning
摘要
Abstract
In this paper,we propose a local outlier detection algorithm based on multi-instance learning (LOF-MIL).In our approach,polysemous objects are abstracted to a multi-instance using an MIL framework,then the MIL-LOF calculates the comprehensive outlier factor and detects outliers by adopting degradation strategies and making weight adjustments.We compared our approach with the classic local outlier detection algorithm and its optimization algorithm on both public and real data sets.Experimental results show that our method a-chieves better accuracy,comprehesiveness,and efficiency.关键词
机器学习/局部离群点/多示例学习/综合离群点因子Key words
machine learning/local outlier/multi-instance learning/comprehensive outlier factor分类
信息技术与安全科学引用本文复制引用
钱景辉,窦立阳,李荣雨..一种基于多示例学习的局部离群点检测算法[J].信息与控制,2016,45(4):471-478,8.基金项目
江苏省高校自然科学基金资助项目 （）
