A re-examination of text categorization methods
Proceedings of the 22nd 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
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A novel refinement approach for text categorization
Proceedings of the 14th ACM international conference on Information and knowledge management
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
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Hierarchical Text Categorization refers to assigning of one or more suitable category from a hierarchical category space to a document. In this paper, we used hierarchical feature selection method and multiple classifiers for the Hierarchical text categorization task. Experiments showed that the methods we used was effective, compared with flat classification, top-down level-based approach with the multiple feature selection method, the single classifier obtained better performance; reliability function was introduction to evaluate the determine by single classifier reliability, if the reliability function got a small value, multiple classifiers were used to give the determine which category the unlabeled document belong to, compared to single classifier, Multiple classifiers achieved better performance on flat and hierarchical corpuses, and the time cost increasing is little than using single main classifier.