KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Outlier detection based on local minima density | |
| Liu, Jia1; Wang, Guoyin2 | |
| 2016 | |
| 摘要 | Outlier is great concern in machine learning task and the traditional methods based on nearest neighbor outlier detection have some weaknesses: performance is sensitive to parameter k, and interpretability is not strong. In this paper, based on the novel idea that outlier objects have lower density than their neighbors and relatively large distance from objects with higher density, we propose a new algorithm for outlier detection to overcome the weakness above. We compared the proposed method with other existing methods based on various types of synthetic datasets. We also applied the proposed method in real water quality data. The results of the numerical experiments indicated that the proposed method has better effectiveness, stability, and interpretability on the detection of low-density outlier detection. © 2016 IEEE. |
| 语种 | 英语 |
| DOI | 10.1109/ITNEC.2016.7560455 |
| 会议(录)名称 | 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2016
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| 页码 | 718-723 |
| 收录类别 | EI |
| 会议地点 | Chongqing, China |
| 会议日期 | May 20, 2016 - May 22, 2016 |
