文献类型：期刊文献

英文题名：Semi-supervised support vector classification with self-constructed Universum

作者：Tian, Yingjie[1,2];Zhang, Ying[3];Liu, Dalian[4]

第一作者：Tian, Yingjie

通讯作者：Liu, DL[1]

机构：[1]Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[2]Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China;[3]Univ Chinese Acad Sci, Sch Math Sci, Beijing 100190, Peoples R China;[4]Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China

第一机构：Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China.|[1141788]北京联合大学基础教学部;[11417]北京联合大学;

年份：2016

卷号：189

起止页码：33-42

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20160801965545);Scopus(收录号：2-s2.0-84968324836);WOS:【SCI-EXPANDED(收录号:WOS:000374802500004)】；

基金：This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 61472390, 11271361, 71331005), Major International (Regional) Joint Research Project (No. 71110107026), "New Start" Academic Research Project of Beijing Union University (No. ZK10201409) and the Ministry of water resources special funds for scientific research on public causes (No. 201301094).

语种：英文

外文关键词：Semi-supervised; Classification; Universum; Support vector machine

摘要：In this paper, we propose a strategy dealing with the semi-supervised classification problem, in which the support vector machine with self-constructed Universum is iteratively solved. Universum data, which do not belong to either class of interest, have been illustrated to encode some prior knowledge by representing meaningful concepts in the same domain as the problem at hand. Our new method is applied to seek more reliable positive and negative examples from the unlabeled dataset step by step, and the Universum support vector machine(U-SVM) is used iteratively. Different Universum data will result in different performance, so several effective approaches are explored to construct Universum datasets. Experimental results demonstrate that appropriately constructed Universum will improve the accuracy and reduce the number of iterations. (C) 2016 Published by Elsevier B.V.