Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning

Active learning provides a solution for annotating huge pools of data efficiently to use it for mining and business analytics. Therefore, it reduces the number of instances that have to be annotated by an expert to the most informative ones. A common approach is to use uncertainty sampling in combination with a support vector machine (SVM). Some papers argue that uncertainty sampling performs badly due to missing exploration, others report good results using an SVM. This paper investigates whether uncertainty sampling is able to explore the data space due to the kernel trick used by the SVMs. Hence, we evaluate this on multiple synthetic and real datasets and the effects of parameter tuning and kernel selection for different evaluation criteria.