Effective label acquisition for collective classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Reflect and correct: A misclassification prediction approach to active inference
ACM Transactions on Knowledge Discovery from Data (TKDD)
Cautious Collective Classification
The Journal of Machine Learning Research
Superstate identification for state machines using search-based clustering
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Combining link and content for collective active learning
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Batch Mode Active Learning for Networked Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Active learning and inference method for within network classification
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Active inference seeks to maximize classification perfor- mance while minimizing the amount of data that must be labeled ex ante. This task is particularly relevant in the context of relational data, where statistical dependencies among instances can be exploited to improve classification accuracy. We show that efficient methods for indexing net- work structure can be exploited to select high-value nodes for labeling. This approach substantially outperforms ran- dom selection and selection based on simple measures of local structure. We demonstrate the relative effectiveness of this selection approach through experiments with a rela- tional neighbor classifier on a variety of real and synthetic data sets, and identify the necessary characteristics of the data set that allow this approach to perform well.