The nature of statistical learning theory
The nature of statistical learning theory
Hierarchical classification of Web content
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
On feature distributional clustering for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
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
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Topic hierarchy generation via linear discriminant projection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Hierarchical multi-classifier system design based on evolutionary computation technique
Multimedia Tools and Applications
Practical application of associative classifier for document classification
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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In a text categorization task, classification on some hierarchy of classes shows better results than the case without the hierarchy. In current environments where large amount of documents are divided into several subgroups with a hierarchy between them, it is more natural and appropriate to use a hierarchical classification method. We introduce a new internal node evaluation scheme which is very helpful to the development process of a hierarchical classifier. We also show that the hierarchical classifier construction method using this measure yields a classifier with better classification performance especially when applied to the classification task with large depth of hierarchy.