Graph-based KNN text classification
Zonghu Wang, Zhijing Liu · 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery · 2010
Vector space model is used in most text categorization methods without considering the important information such as the order and co- occurrence of words within the text. In this paper we describe a novel approach of text classification using graph-based KNN. We reduce the number of features dimensions by a combined feature selection method. Then we present an improved graph-based text representation model and describe a novel graph-based KNN algorithm to predict the category of the texts in the testing set. The result shows that our approach can outperform traditional VSM-based KNN methods in terms of both accuracy and cost time.