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Learning Contextual Dependency Network Models for Link-Based Classification
November 2006 (vol. 18 no. 11)
pp. 1482-1496
Links among objects contain rich semantics that can be very helpful in classifying the objects. However, many irrelevant links can be found in real-world link data such as Web pages. Often, these noisy and irrelevant links do not provide useful and predictive information for categorization. It is thus important to automatically identify which links are most relevant for categorization. In this paper, we present a contextual dependency network (CDN) model for classifying linked objects in the presence of noisy and irrelevant links. The CDN model makes use of a dependency function that characterizes the contextual dependencies among linked objects. In this way, CDNs can differentiate the impacts of the related objects on the classification and consequently reduce the effect of irrelevant links on the classification. We show how to learn the CDN model effectively and how to use the Gibbs inference framework over the learned model for collective classification of multiple linked objects. The experiments show that the CDN model demonstrates relatively high robustness on data sets containing irrelevant links.

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Index Terms:
Data dependencies, hypertext/hypermedia, machine learning, link-based classification, link context, contextual dependency networks, Gibbs inference.
Citation:
Yonghong Tian, Qiang Yang, Tiejun Huang, Charles X. Ling, Wen Gao, "Learning Contextual Dependency Network Models for Link-Based Classification," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 11, pp. 1482-1496, Nov. 2006, doi:10.1109/TKDE.2006.178
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