Active Learning for Multiclass Cost-Sensitive Classification Using Probabilistic Models.
Journal
Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013, Taipei, Taiwan, December 6-8, 2013
Pages
13-18
Date Issued
2013
Author(s)
Chen, Po-Lung
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.
Type
conference paper