Active learning with direct query construction

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Active learning with direct query construction

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International Conference on Knowledge Discovery and Data Mining archive
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Las Vegas, Nevada, USA

SESSION: Research papers table of contents

Pages 480-487

Year of Publication: 2008

ISBN:978-1-60558-193-4

Authors

Charles X. Ling	The University of Western Ontario, London, ON, Canada
Jun Du	The University of Western Ontario, London, ON, Canada

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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ABSTRACT

Active learning may hold the key for solving the data scarcity problem in supervised learning, i.e., the lack of labeled data. Indeed, labeling data is a costly process, yet an active learner may request labels of only selected instances, thus reducing labeling work dramatically. Most previous works of active learning are, however, pool-based; that is, a pool of unlabeled examples is given and the learner can only select examples from the pool to query for their labels. This type of active learning has several weaknesses. In this paper we propose novel active learning algorithms that construct examples directly to query for labels. We study both a specific active learner based on the decision tree algorithm, and a general active learner that can work with any base learning algorithm. As there is no restriction on what examples to be queried, our methods are shown to often query fewer examples to reduce the predictive error quickly. This casts doubt on the usefulness of the pool in pool-based active learning. Nevertheless, our methods can be easily adapted to work with a given pool of unlabeled examples.

REFERENCES

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