Selective Sampling with Redundant Views

Selective sampling, a form of active learning, reduces the cost of labeling training data by asking only for the labels of the most informative unlabeled examples. We introduce a novel approach to selective sampling which we call co-testing. Co-testing can be applied to problems with redundant views (i. e. , problems with multiple disjoint sets of attributes that can be used for learning). We analyze the most general algorithm in the cotesting family, naive co-testing, which can be used with virtually any type of learner. Naive co-testing simply selects at random an example on which the existing views disagree. We applied our algorithm to a variety of domains, including three real-world problems: wrapper induction, Web page classification, and discourse trees parsing. The empirical results show that besides reducing the number of labeled examples, naive co-testing may also boost the classification accuracy.

Authors

• Ion Muslea Thomas J. Lee
• and Craig A. Knoblock University of Southern California

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