Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics)
Social ties and their relevance to churn in mobile telecom networks
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Ensembles of relational classifiers
Knowledge and Information Systems
Towards Machine Learning on the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Why Stacked Models Perform Effective Collective Classification
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Cautious inference in collective classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Evaluating Statistical Tests for Within-Network Classifiers of Relational Data
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Cautious Collective Classification
The Journal of Machine Learning Research
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Semi-Supervised Learning
Graph-Based model-selection framework for large ensembles
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Validation of network classifiers
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Feature enrichment and selection for transductive classification on networked data
Pattern Recognition Letters
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Collective classification algorithms have been used to improve classification performance when network training data with content, link and label information and test data with content and link information are available. Collective classification algorithms use a base classifier which is trained on training content and link data. The base classifier inputs usually consist of the content vector concatenated with an aggregation vector of neighborhood class information. In this paper, instead of using a single base classifier, we propose using different types of base classifiers for content and link. We then combine the content and link classifier outputs using different classifier combination methods. Our experiments show that using heterogeneous classifiers for link and content classification and combining their outputs gives accuracies as good as collective classification. Our method can also be extended to collective classification scenarios with multiple types of content and link.