Exponentiated Gradient Exploration for Active Learning
This work addresses the labeling cost issue for machine learning practitioners, offering an incremental improvement over existing active learning methods.
The paper tackles the problem of costly labeling in supervised classification by proposing EG-Active, a sequential algorithm that enhances any active learning method with optimal random exploration, resulting in statistically significant and appreciable performance improvements.
Active learning strategies respond to the costly labelling task in a supervised classification by selecting the most useful unlabelled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can be more informative. In this setting, we propose a sequential algorithm named EG-Active that can improve any Active learning algorithm by an optimal random exploration. Experimental results show a statistically significant and appreciable improvement in the performance of our new approach over the existing active feedback methods.