Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Acclimatizing Taxonomic Semantics for Hierarchical Content Classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Hierarchical document classification using automatically generated hierarchy
Journal of Intelligent Information Systems
Topic taxonomy adaptation for group profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
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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We focused on taxonomy modification algorithms for gradually improving the relevance performances of large-scale hierarchical classifiers of web documents. Considering the research results of Tang et al. [5,4], who took the same approach, we investigated and implemented two heuristic taxonomy modification algorithms for performing practical classification processes for large-scale taxonomies. Although a taxonomy modification algorithm continuously improves the relevance performances of hierarchical classifiers, it increases the computational costs of those classifiers for training and predicting processes. We developed an improved taxonomy modification algorithm for reducing computational costs by preventing child node concentration. Although the relevance performances of the algorithm-modified taxonomy classifiers improved without increasing computational costs until the fourth generation by spreading the set of predicted classes, their relevance performances and behaviors went in opposite directions from the fifth generation.