A Graph-Based Method for Active Outlier Detection With Limited Expert Feedback
Labeled data, particularly for the outlier class, are difficult to obtain. Thus, outlier detection is typically regarded as an unsupervised learning problem. However, it still has an opportunity to obtain few labeled data. For example, a human analyst can give feedback to few data when he/she examin...
Main Authors: | Yongmou Li, Yijie Wang, Xingkong Ma, Cheng Qian, Xiaoyong Li |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8871105/ |
Similar Items
-
Similarity Distribution Density: An Optimized Approach to Outlier Detection
by: Li Quan, et al.
Published: (2023-10-01) -
Outlier Recognition via Linguistic Aggregation of Graph Databases
by: Adam Niewiadomski, et al.
Published: (2021-08-01) -
Interpreting Deep Graph Convolutional Networks with Spectrum Perspective
by: Sisi Zhang, et al.
Published: (2023-05-01) -
Outlier Detection with Reinforcement Learning for Costly to Verify Data
by: Michiel Nijhuis, et al.
Published: (2023-05-01) -
A survey of large-scale graph-based semi-supervised classification algorithms
by: Yunsheng Song, et al.
Published: (2022-06-01)