2008 ActiveLearningwithDirectQueryCo

• (Ling et al., 2008) ⇒ Charles X. Ling, and Jun Du. (2008). “Active Learning with Direct Query Construction” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401950

Subject Headings:

Cited By

• Google Scholar: 19 Citations
• ACM DL: 4 Citations
• Semantic Scholar: 12 Citations

Quotes

Author Keywords

Active Learning, Classification, Supervised Learning.

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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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ActiveLearningwithDirectQueryCo	Jun Du Charles X. Ling			Active Learning with Direct Query Construction		KDD-2008 Proceedings		10.1145/1401890.1401950		2008