Balancing exploration and exploitation: a new algorithm for active machine learning

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 r...

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Bibliographic Details
Published inFifth IEEE International Conference on Data Mining (ICDM'05) p. 8 pp.
Main Authors Osugi, T., Deng Kim, Scott, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Application software
Computer science
Feedback
Humans
Labeling
Machine learning
Machine learning algorithms
Region 4
Sampling methods
Text categorization
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Summary: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.
ISBN:9780769522784
0769522785
ISSN:1550-4786
2374-8486
DOI:10.1109/ICDM.2005.33