Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency Networks for Relational Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Leveraging Relational Autocorrelation with Latent Group Models
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Autocorrelation and linkage cause bias in evaluation of relational learners
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Effective label acquisition for collective classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Case-Based Collective Inference for Maritime Object Classification
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Identifying graphs from noisy and incomplete data
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
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
Identifying graphs from noisy and incomplete data
ACM SIGKDD Explorations Newsletter
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Learning what and how of contextual models for scene labeling
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Collective graph identification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
On the semantic annotation of places in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Collective classification using heterogeneous classifiers
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Label-dependent node classification in the network
Neurocomputing
Leveraging Network Properties for Trust Evaluation in Multi-agent Systems
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Leveraging high-level and low-level features for multimedia event detection
Proceedings of the 20th ACM international conference on Multimedia
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Sparsification and sampling of networks for collective classification
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Uncovering collusive spammers in Chinese review websites
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Collective classification can significantly improve accuracy by exploiting relationships among instances. Although several collective inference procedures have been reported, they have not been thoroughly evaluated for their commonalities and differences. We introduce novel generalizations of three existing algorithms that allow such algorithmic and empirical comparisons. Our generalizations permit us to examine how cautiously or aggressively each algorithm exploits intermediate relational data, which can be noisy. We conjecture that cautious approaches that identify and preferentially exploit the more reliable intermediate data should outperform aggressive approaches. We explain why caution is useful and introduce three parameters to control the degree of caution. An empirical evaluation of collective classification algorithms, using two base classifiers on three data sets, supports our conjecture.