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
Deep classification in large-scale text hierarchies
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Refined experts: improving classification in large taxonomies
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
International Journal of Data Mining and Bioinformatics
On empirical tradeoffs in large scale hierarchical classification
Proceedings of the 21st ACM international conference on Information and knowledge management
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Going beyond the traditional text classification, involving a few tens of classes, there has been a surge of interest in automatic document categorization in large taxonomies where the number of classes range from hundreds of thousands to millions. Due to the complex nature of the learning problem posed in such scenarios, one needs to adapt the conventional classification schemes to suit this domain. This paper presents a novel approach for classifier selection in large hierarchies, which is based on exploiting training data heterogeneity across the hierarchy. We also present a meta-learning framework for further flexibility in classifier selection. The experimental results demonstrate the applicability of our approach, which achieves accuracy comparable to the state-of-the-art and is also significantly faster for prediction.