Original title: Text document classification
Translated title: Klasifikace textových dokumentů
Authors: Humpolíček, Jiří
Document type: Research reports
Year: 2006
Language: eng
Series: Research Report, volume: 2175
Abstract: In this report, we propose four feature selection algorithms based on the Best Individual Feature method and one based on the sequential method. After that the best method is selected for following classifier methods comparison. In this step we compare classification performance and computation expense of two classifiers based on Naive Bayes and third classifier is SVM. Classification performance is tested on the Reuters data set and Newsgroup data set. Finally we shows results on the multi-labelled subset of the Reuters data set.
Keywords: classification; text document
Project no.: CEZ:AV0Z10750506 (CEP)

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at the institute of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0139697

Permalink: http://www.nusl.cz/ntk/nusl-36266


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Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports