%0 Journal Article
%T Optimization Algorithm for Multivariate Decision Trees Based on VPRS<br>基于VPRS多变量决策树优化算法
%A 邱云飞
%A 王光
%A 关晓林
%A 邵良杉
%J 计算机系统应用
%D 2010
%I 
%X When construct multivariate decision trees, noise data reduced the training efficiency and quality of model, most of the present pruning methods aimed at leaf node to eliminate the influence of noise data, but not pay attention to the disturbed problem of noise data when selected testing attribute. In order to solve the problem, extends the relative core of attributes in rough sets theory to variable precision rough set(VPRS), and uses it for selection of initial variables for decision tree; extends the concept of generalization of one equivalence relation with respect to another one, to relative generalization equivalence relation under mostly-contained condition, and uses it for decision tree initial attribute check;propose an algorithm for multivariate decision tree that can avoid disturbance of noisy data. Finally, validated the algorithm by an experiment.
%K univariate decision trees
%K multivariate decision trees
%K noisy data
%K variable precision rough set
%K relative core of attributes<br>单变量决策树
%K 多变量决策树
%K 噪声数据
%K 变精度粗糙集
%K 相对核
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D4F6864C950C88FFCE5B6C948A639E39&aid=B0A3E87A384EB258F5B78B739F0FF636&yid=140ECF96957D60B2&vid=2A8D03AD8076A2E3&iid=59906B3B2830C2C5&sid=58F693790F887B3B&eid=B47A0E731AF43EB2&journal_id=1003-3254&journal_name=计算机系统应用&referenced_num=0&reference_num=10