[1]叶云龙,杨明.一种基于多模态模型的随机子空间分类集成算法[J].南京师范大学学报(工程技术版),2009,09(04):057-62.
 Ye Yunlong,Yang Ming.A Multi-modality-based Random Subspace Classifier Ensemble Algorithm[J].Journal of Nanjing Normal University(Engineering and Technology),2009,09(04):057-62.
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一种基于多模态模型的随机子空间分类集成算法
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
09卷
期数:
2009年04期
页码:
057-62
栏目:
出版日期:
2009-12-30

文章信息/Info

Title:
A Multi-modality-based Random Subspace Classifier Ensemble Algorithm
作者:
叶云龙;杨明;
南京师范大学计算机科学与技术学院, 江苏南京210097
Author(s):
Ye YunlongYang Ming
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210097,China
关键词:
多模态 随机子空间 分类器集成
Keywords:
M ult-im odality random subspace classifier ensemb le
分类号:
TP301.6
摘要:
分类是当前机器学习的重要研究内容之一,已取得了一定的进展.现有的文本分类方法大多基于VSM模型,而VSM未能有效地利用隐含在文本中的结构信息.同时,VSM下的样本空间常常是高维的,单一的降维策略可能会丢失有用信息.为改进现有算法的不足,提出了一种基于多模态模型的随机子空间分类集成算法MMRFSEn,有效地利用文本中的结构信息(单词分布位置的均值和标准差),且各基分类器是由随机选择的子空间构建而成.实验结果表明,该方法是有效可行的.
Abstract:
Tex t C lassifica tion is an im portant m ach ine learn ing research, in w hich som e progress has been made. M ost o f the ex isting class ification me thods are based on Vecto r SpaceM ode l( VSM ), but VSM does not e ffective ly u tilize the structure in fo rm ation h idden in the text sam ples. A t the same tim e, VSM vectors are o ften h igh-d im ensiona,l m ere ly us ing d im ensiona lity reduction stra tegy m ay lead to the lo ss of the use fu l in fo rm ation. To overcom e the shortcom ings o f the ex isting a lgo rithm s, w e propose an algorithm ca lledM ult-i modality-based Random Feature subspace classifier Ensem ble (MMRFSEn) , wh ich can e ffective ly use the structure in fo rm ation h idden in the text such as the w ords’ s average location and standa rd dev ia tion, and m eanw hile each sing le class ifier is constructed by a random ly se lected subspace. The experim ental resu lts show tha t the new ly deve loped m e thod is e ffective and feasib le.

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金( 60873176)、江苏省自然科学基金( BK2008430)资助项目.
通讯联系人: 杨 明, 教授, 博士生导师, 研究方向: 数据挖掘, 机器学习, 模式识别, 粗集理论. E-m ail:m. yang@ n jnu. edu. cn
更新日期/Last Update: 2013-04-23