Active learning is an important technique to improve the learned model using unlabeled data, when labeled data is difficult
to obtain, and unlabeled data is available in large quantity and easy to collect. Several instance querying strategies have
been suggested recently. These works show that empirical risk minimization (ERM) can find the next instance to label
effectively, but the computation time consumption is large. This paper introduces a new approach to select the best
instance with less time consumption. In the case where the data is graphical in nature, we can implement the graph
topological analysis to rapidly select instances that are likely to be good candidates for labeling. This paper describes an
approach of using degree of a node metric to identify the best instance next to label. We experiment on Zachary's Karate
Club dataset and 20 newsgroups dataset with four binary classification tasks, the results show that the strategy of degree of
a node has similar performance to ERM with less time consumption.
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