%0 Journal Article
%T Framework of Classification Based on Multi-Value Decomposition and Multi-Label Learning<br>基于多值分解和多类标学习的分类框架设计
%A SEEN Liang-Zhong
%A CHEN Sheng-Kai
%A HU Jie-Zhen
%A <br>沈良忠
%A 陈胜凯
%A 胡捷臻
%J 计算机系统应用
%D 2010
%I 
%X Classification of multi-valued and multi-labeled data is about a sample which is not only associated with a set of labels, but also with several values that include some attributes. This paper proposes a multi-valued and multi-labeled learning framework that combines multi-value decomposition with multi-label learning (MDML), using four strategies to deal with multi-valued attributes and three classical, multi-label algorithms to learn. Experimental results demonstrate that MDML significantly outperforms the decision tree based method. Meanwhile, combined methods can be applied to various types of datasets.
%K classification
%K multi-label data
%K multi-valued attribute decomposition
%K data transformation<br>分类
%K 多值属性分解
%K 多类标数据
%K 数据转化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D4F6864C950C88FFCE5B6C948A639E39&aid=114B71C3F69A4C1B6D2D0875F8823348&yid=140ECF96957D60B2&vid=2A8D03AD8076A2E3&iid=F3090AE9B60B7ED1&sid=3E0812ED84A7B31D&eid=6235172E4DDBA109&journal_id=1003-3254&journal_name=计算机系统应用&referenced_num=0&reference_num=7