Induction of predictive models is one of the most frequent data mining tasks. However, for several domains, the available data is unlabeled and the generation of a class label for each instance may have a high cost. An alternative to reduce this cost is the use of active learning, which selects instances according to a criterion of relevance. Diverse sampling strategies for active learning, following different paradigms, can be found in the literature. However, there is no detailed comparison between these strategies and they are usually evaluated for only one classification technique. In this paper, strategies from different paradigms are experimentally compared using different learning algorithms and datasets. Additionally, a multiclass hypothesis space search called SG-multi is proposed and empirically shown to be feasible. Experimental results show the effectiveness of active learning and which classification techniques are more suitable to which sampling strategies.
['Davi Pereira dos Santos', 'André Carlos Ponce Leon Ferreira de Carvalho']
Comparison of Active Learning Strategies and Proposal of a Multiclass Hypothesis Space Search