Machine Learning - Special issue on inductive transfer
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Exploiting Hierarchy in Text Categorization
Information Retrieval
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Exploiting known taxonomies in learning overlapping concepts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Hierarchical multi-class text categorization with global margin maximization
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
An effective feature selection method for text categorization
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Arabic Text Categorization Based on Arabic Wikipedia
ACM Transactions on Asian Language Information Processing (TALIP)
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Recently, hierarchical text classification has become an active research topic. The essential idea is that the descendant classes can share the information of the ancestor classes in a predefined taxonomy. In this paper, we claim that each class has several latent concepts and its subclasses share information with these different concepts respectively. Then, we propose a variant Passive-Aggressive (PA) algorithm for hierarchical text classification with latent concepts. Experimental results show that the performance of our algorithm is competitive with the recently proposed hierarchical classification algorithms.