@inproceedings{80923ab188bb44c3ad4830f2790e5453,
title = "An improved growing LVQ for text classification",
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.",
keywords = "KNN, Learning vector quantification, Reference sample, Text classification",
author = "Xiujun Wang and Hong Shen",
year = "2009",
doi = "10.1109/FSKD.2009.340",
language = "English",
isbn = "9780769537351",
series = "6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009",
pages = "114--118",
booktitle = "6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009",
note = "6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 ; Conference date: 14-08-2009 Through 16-08-2009",
}