Name
Title | ||
|---|---|---|
Active Learning for Multiclass Cost-Sensitive Classification Using Probabilistic Models |
Abstract | ||
|---|---|---|
Multiclass cost-sensitive active learning is a relatively new problem. In this paper, we derive the maximum expected cost and cost-weighted minimum margin strategies for multiclass cost-sensitive active learning. The two strategies can be viewed as extended versions of the classical cost-insensitive active learning strategies. The experimental results demonstrate that the derived strategies are promising for cost-sensitive active learning. In particular, the cost-sensitive strategies out-perform cost-insensitive ones on many benchmark data-sets and justify that an appropriate consideration of the cost information is important for solving cost-sensitive active learning problems. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TAAI.2013.17 | TAAI |
Keywords | Field | DocType |
cost-sensitive active learning problem,active learning,probabilistic models,cost information,appropriate consideration,maximum expected cost,classical cost-insensitive active learning,learning (artificial intelligence),multiclass,pattern classification,multiclass cost-sensitive active learning,cost-weighted minimum margin strategy,classical cost-insensitive active learning strategies,benchmark data-sets,cost-sensitive active learning,cost-sensitive,multiclass cost-sensitive classification,cost-sensitive strategy,cost-weighted minimum margin strategies,probability,learning artificial intelligence | Semi-supervised learning,Active learning,Active learning (machine learning),Computer science,Artificial intelligence,Probabilistic logic,Expected cost,Machine learning,Multiclass classification | Conference |
ISSN | ISBN | Citations |
2376-6816 | 978-1-4799-2528-5 | 2 |
PageRank | References | Authors |
0.36 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Po-Lung Chen | 1 | 27 | 3.28 |
| Hsuan-Tien Lin | 2 | 829 | 74.77 |