An improved growing LVQ for text classification

Date

2009

Authors

Wang, X.
Shen, H.

Editors

Advisors

Journal Title

Journal ISSN

Volume Title

Type:

Conference paper

Citation

Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) 2009: pp.114-118

Statement of Responsibility

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.

School/Discipline

Dissertation Note

Provenance

Description

Access Status

Rights

© 2009 IEEE

License

Grant ID

Call number

Persistent link to this record