An improved growing LVQ for text classification

Xiujun Wang, Hong Shen

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
頁面114-118
頁數5
DOIs
出版狀態Published - 2009
對外發佈
事件6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 - Tianjin, China
持續時間: 14 8月 200916 8月 2009

出版系列

名字6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
1

Conference

Conference6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
國家/地區China
城市Tianjin
期間14/08/0916/08/09

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