%0 Journal Article
%T Optimized k-NN Text Categorization Approach<br>一种优化的k-NN文本分类算法
%A YAN Peng
%A ZHENG Xue-feng
%A ZHU Jian-yong
%A XIAO Yun-hong
%A <br>闫鹏
%A 郑雪峰
%A 朱建勇
%A 肖赟泓
%J 计算机科学
%D 2009
%I 
%X As one of the most classical TC approaches,k-NN is advantaged in tackling concept drift.However,to avoid curse of dimensionality,it has to employ FS(feature selection)method to reduce dimensionality of feature space and improve operation efficiency.But FS process will generally cause information losing and thus has some side-effects on the whole performance of approach.According to sparsity of text vectors,an optimized k-NN approach was presented in paper.This optimized approach greatly simplified euclidean...
%K Text categorization
%K Feature selection
%K k-NN
%K Concept drift<br>文本分类
%K 特征选择
%K k-NN分类法
%K 概念漂移
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=1020D348D76B1D3B81F1FD965712E44B&yid=DE12191FBD62783C&vid=933658645952ED9F&iid=F3090AE9B60B7ED1&sid=F9F74EC1AA08A7B9&eid=78F0EFE028BD3783&journal_id=1002-137X&journal_name=计算机科学&referenced_num=1&reference_num=12