Making large-scale support vector machine learning practical
Advances in kernel methods
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
The VLDB Journal — The International Journal on Very Large Data Bases
Acclimatizing Taxonomic Semantics for Hierarchical Content Classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic taxonomy adaptation for group profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
Discovering relationships among categories using misclassification information
Proceedings of the 2008 ACM symposium on Applied computing
Detecting relationships among categories using text classification
Journal of the American Society for Information Science and Technology
Multi-label classification and extracting predicted class hierarchies
Pattern Recognition
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Editorial: Narrative-based taxonomy distillation for effective indexing of text collections
Data & Knowledge Engineering
Category hierarchy maintenance: a data-driven approach
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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While several hierarchical classification methods have been applied to web content, such techniques invariably rely on a pre-defined taxonomy of documents. We propose a new technique that extracts a suitable hierarchical structure automatically from a corpus of labeled documents. We show that our technique groups similar classes closer together in the tree and discovers relationships among documents that are not encoded in the class labels. The learned taxonomy is then used along with binary SVMs for multi-class classification. We demonstrate the efficacy of our approach by testing it on the 20-Newsgroup dataset.