%0 Journal Article
%T Data Mining on Imbalanced Data Sets<br>用于不均衡数据集的挖掘方法
%A ZHAO Feng-Ying
%A WANG Chong-Jun
%A CHEN Shi-Fu
%A <br>赵凤英
%A 王崇骏
%A 陈世福
%J 计算机科学
%D 2007
%I 
%X The majority of machine learning algorithms previously designed usually assume that their training sets are well-balanced,but data in real-world is usually imbalanced.The tradition machine learning algorithms on balanced data sets have bad performance when they learn from imbalaneed data sets.Thus,machine learning on imbalanced data sets becomes an urgent problem.In this paper,a simple review of the related work is informed.
%K Imbalance data set
%K Over-sampling
%K Under-sampling Cost-sensitive learning<br>不均衡数据集
%K 过取样
%K 欠取样
%K 代价敏感学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=7C83B6CE0AD213BE&yid=A732AF04DDA03BB3&vid=339D79302DF62549&iid=9CF7A0430CBB2DFD&sid=12DC19455C3A2FA8&eid=A8DE7703CC9E390F&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=19