| Year | Title | Author | Publication | Code | Tasks | Notes | Datasets | Notions |
|---|---|---|---|---|---|---|---|---|
| 2019 | Online Active Learning Ensemble Framework for Drifted Data Streams | Shan et al. | IEEE TNNLS | - | hybrid, data streams |
This paper focus on data streams based active learning and solve the concept drift between successive time. | Not pool-based active learning methods. | |
| 2019 | Cost-Effective Object Detection: Active Sample Mining With Switchable Selection Criteria | Wang et al. | IEEE TNNLS | Impor Code |
Objective detection | AL+Pseudo-labeled, `` |
PASCAL VOC 2007/2012 datasets | low confidence: human annotation; High confidence: Pseudo labeled |
| 2019 | Active Learning From Imbalanced Data: A Solution of Online Weighted Extreme Learning Machine | Yu et al. | IEEE TNNLS | - | Uncertainty, Clustering-based initialization, Margin exhaustion criterion: Stopping criterion, Imbalance Problem |
University of California-Irvine (UCI) machine learning data repository [50], and the others are from several publications about bioinformatics [51]–[53]. | ||
| 2019 | Pool-Based Sequential Active Learning for Regression | Wu | IEEE TNNLS | - | Regression |
three essential criteria, New Hybrid Sampling, |
University of California, Irvine, Carnegie Mellon University StatLib, and University of Florida Media Core data sets | We then propose a new ALR approach using passive sampling, which considers both the representativeness and the diversity in both the initialization and subsequent iterations. |
| 2019 | Bag-Level Aggregation for Multiple-Instance Active Learning in Instance Classification Problems | Carbonneau et al. | IEEE TNNLS | - | multiple instance selection, New informativeness based method, clustering-based method |
Birds [16], SIVAL and Newsgroups. |