Text and Document Classification Technologies
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Scope of this subject
This cluster of papers focuses on the application of machine learning algorithms for multi-label text classification, with an emphasis on techniques such as feature selection, Naive Bayes classifier, K-nearest Neighbor (KNN), hierarchical classification, and support vector machines (SVM). The research covers various aspects of document categorization and information retrieval in the context of text mining and natural language processing.
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Research vocabulary
text classification; multi-label classification; document classification; web page classification; news classification; label correlation; multi-label text classification; term weighting; short text classification; topic classification; Arabic text classification; Reuters-21578; Chinese text classification; multi-label feature selection; 20 Newsgroups; hierarchical text classification; automatic document classification; label dependencies; multi-class text classification; binary relevance; text document classification; document frequency; classifier chains; missing labels; Hamming loss
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Morton address: 36183038963125459 · morton4x16-v1
Coordinates: (3, 17, 1702, 11550). The address interleaves categorical identifier bits. Nearby addresses are not guaranteed to describe semantically similar subjects.
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