Thomas Osugi, Deng Kun, and Stephen Scott. Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning. In Proceedings of the Fifth IEEE International Conference on Data Mining, pages 330–337. November 2005.
Abstract
paper (340Kb PDF)

Abstract

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.

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Last modified 16 September 2005.