Name
Abstract | ||
|---|---|---|
Learning of classifiers to be used as filters within the analytical reasoning process leads to new and aggravates existing challenges. Such classifiers are typically trained ad-hoc, with tight time constraints that affect the amount and the quality of annotation data and, thus, also the users' trust in the classifier trained. We approach the challenges of ad-hoc training by inter-active learning, which extends active learning by integrating human experts' background knowledge to greater extent. In contrast to active learning, not only does inter-active learning include the users' expertise by posing queries of data instances for labeling, but it also supports the users in comprehending the classifier model by visualization. Besides the annotation of manually or automatically selected data instances, users are empowered to directly adjust complex classifier models. Therefore, our model visualization facilitates the detection and correction of inconsistencies between the classifier model trained by examples and the user's mental model of the class definition. Visual feedback of the training process helps the users assess the performance of the classifier and, thus, build up trust in the filter created. We demonstrate the capabilities of inter-active learning in the domain of video visual analytics and compare its performance with the results of random sampling and uncertainty sampling of training sets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/VAST.2012.6400492 | Visual Analytics Science and Technology |
Keywords | Field | DocType |
cognition,data visualisation,inference mechanisms,information filtering,information filters,learning (artificial intelligence),pattern classification,training,video retrieval,ad-hoc classifier training process visual feedback,analytical reasoning process,classifier learning,data annotation quality,data labeling queries,data visualization,human expert background knowledge,inter-active learning,time constraints,user expertise,user mental model,video visual analytics,h.3.3 [information systems]: information storage and retrieval — information search and retrieval,i.2.6 [computing methodologies]: artificial intelligence — learning,learning artificial intelligence | Data modeling,Data mining,Data visualization,Stability (learning theory),Active learning,Visualization,Computer science,Visual analytics,Artificial intelligence,Classifier (linguistics),Machine learning,Learning classifier system | Conference |
ISSN | ISBN | Citations |
2325-9442 | 978-1-4673-4752-5 | 8 |
PageRank | References | Authors |
0.42 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Hoferlin | 1 | 110 | 7.00 |
| rudolf netzel | 2 | 10 | 1.82 |
| Markus Hoferlin | 3 | 38 | 2.32 |
| daniel weiskopf | 4 | 8 | 0.42 |
| Gunther Heidemann | 5 | 454 | 48.16 |