An improved growing LVQ for text classification

Xiujun Wang, Hong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

KNN 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.

Original languageEnglish
Title of host publication6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Pages114-118
Number of pages5
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 - Tianjin, China
Duration: 14 Aug 200916 Aug 2009

Publication series

Name6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Volume1

Conference

Conference6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Country/TerritoryChina
CityTianjin
Period14/08/0916/08/09

Keywords

  • KNN
  • Learning vector quantification
  • Reference sample
  • Text classification

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