An improved growing LVQ for text classification

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dc.contributor.authorWang, X.
dc.contributor.authorShen, H.
dc.contributor.conferenceInternational Conference on Fuzzy Systems and Knowledge (6th : 2009 : Tianjin, China)
dc.date.issued2009
dc.description.abstractKNN as a simple classification method has been widely applied in text classification. There are two problems in KNN-based text classification: the large computation load and the deterioration of classification accuracy caused by the uneven distribution of training samples. To solve these problems, we propose a new growing LVQ method and apply it to text classification based on minimizing the increment of learning errors. Our method can generate a representative sample (reference sample) set after one phase of training of sample set, and hence has a strong learning ability. The experiment shows the improvement on both time and accuracy. For our algorithm, we also proposed a learning sequence arrangement method which performs better than others.
dc.description.statementofresponsibilityXiujun Wang and Hong Shen
dc.identifier.citationProceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) 2009: pp.114-118
dc.identifier.doi10.1109/FSKD.2009.340
dc.identifier.isbn9780769537351
dc.identifier.orcidShen, H. [0000-0002-3663-6591] [0000-0003-0649-0648]
dc.identifier.urihttp://hdl.handle.net/2440/58619
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeUSA
dc.rights© 2009 IEEE
dc.source.urihttps://doi.org/10.1109/fskd.2009.340
dc.subjectText Classification
dc.subjectKNN
dc.subjectLearning Vector Quantification
dc.subjectReference Sample
dc.titleAn improved growing LVQ for text classification
dc.typeConference paper
pubs.publication-statusPublished

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