Name
Abstract | ||
|---|---|---|
Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced. In practice, it is quite difficult to handle imbalanced unl... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TKDE.2019.2955078 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Optimization,Indexes,Adaptation models,Manganese,Sensitivity,Correlation | Journal | 33 |
Issue | ISSN | Citations |
6 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifan Zhang | 1 | 7 | 2.80 |
| Peilin Zhao | 2 | 1365 | 80.09 |
| Shuaicheng Niu | 3 | 2 | 2.06 |
| Wu Qingyao | 4 | 259 | 33.46 |
| Jiezhang Cao | 5 | 16 | 4.30 |
| Junzhou Huang | 6 | 2182 | 141.43 |
| Rui Tang | 7 | 188 | 19.22 |