Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Authors

Thomas Osugi, University of Nebraska-LincolnFollow
Deng Kun, University of Nebraska-LincolnFollow
Stephen Scott, University of Nebraska-LincolnFollow

Date of this Version

2005

Comments

Published in Fifth IEEE International Conference on Data Mining. Copyright 2005 IEEE. Used by permission.

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.