Original Contribution: Stacked generalization
Neural Networks
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Enhanced hypertext categorization using hyperlinks
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Machine Learning
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The Combination of Text Classifiers Using Reliability Indicators
Information Retrieval
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Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
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PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Stacked generalization: when does it work?
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Expert Systems with Applications: An International Journal
Spam Filtering Using Statistical Data Compression Models
The Journal of Machine Learning Research
Improvement of building field association term dictionary using passage retrieval
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Spam filtering for short messages
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Email Spam Filtering: A Systematic Review
Foundations and Trends in Information Retrieval
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This paper examines several different approaches to exploiting structural information in semi-structured document categorization. The methods under consideration are designed for categorization of documents consisting of a collection of fields, or arbitrary tree-structured documents that can be adequately modeled with such a fiat structure. The approaches range from trivial modifications of text modeling to more elaborate schemes, specifically tailored to structured documents. We combine these methods with three different text classification algorithms and evaluate their performance on four standard datasets containing different types of semi-structured documents. The best results were obtained with stacking, an approach in which predictions based on different structural components are combined by a meta classifier. A further improvement of this method is achieved by including the flat text model in the final prediction.