Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
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Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
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Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
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Proceedings of the tenth international conference on Information and knowledge management
Exploiting Hierarchy in Text Categorization
Information Retrieval
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections
Journal of Intelligent Information Systems
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Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The VLDB Journal — The International Journal on Very Large Data Bases
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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The Journal of Machine Learning Research
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
A graph-theoretic framework for semantic distance
Computational Linguistics
Intelligent Data Analysis
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In this paper we propose TreeBoost.MH, an algorithm for multi-label Hierarchical Text Categorization (HTC) consisting of a hierarchical variant of AdaBoost.MH. TreeBoost.MH embodies several intuitions that had arisen before within HTC: e.g. the intuitions that both feature selection and the selection of negative training examples should be performed “locally”, i.e. by paying attention to the topology of the classification scheme. It also embodies the novel intuition that the weight distribution that boosting algorithms update at every boosting round should likewise be updated “locally”. We present the results of experimenting TreeBoost.MH on two HTC benchmarks, and discuss analytically its computational cost.