Batch Mode Active Learning for Networked Data

The authors study a novel problem of batch mode active learning for networked data. In this problem, data instances are connected with links and their labels are correlated with each other, and the goal of batch mode active learning is to exploit the link-based dependencies and node-specific content information to actively select a batch of instances to query the user for learning an accurate model to label unknown instances in the network. They present three criteria (i.e., minimum redundancy, maximum uncertainty and maximum impact) to quantify the informativeness of a set of instances, and formalize the batch mode active learning problem as selecting a set of instances by maximizing an objective function which combines both link and content information.

Provided by: Association for Computing Machinery Topic: Data Management Date Added: Apr 2012 Format: PDF

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