An improved growing LVQ for text classification
Date
2009
Authors
Wang, X.
Shen, H.
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Conference paper
Citation
Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) 2009: pp.114-118
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Xiujun Wang and Hong Shen
Conference Name
International Conference on Fuzzy Systems and Knowledge (6th : 2009 : Tianjin, China)
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.
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© 2009 IEEE