Feature Selection Based on Neighborhood Systems and Rough Set Theory

被引：0
|
作者：
He, Ming [1 ]
机构：
[1] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China
来源：
WKDD: 2009 SECOND INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2009年
关键词：
rough set; neighborhood systems; feature selection;
D O I：
10.1109/WKDD.2009.11
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Attribute reduction is an important issue in data mining and knowledge acquisition. It has been proven that computing all reductions and optimal (minimal) reduction is a NP-hard problem. This paper proposed a hybrid approach using the rough set theory and neighborhood systems for feature selection. Two neighborhood approximation operators are defined based on rough set. A neighborhood rough model is constructed subsequently and the heuristic information is introduced according to the significance of attributes respectively. Experimental results indicate that the proposed method can reduce attributes effectively.
引用
收藏
页码：3 / 5
页数：3
相关论文
共 50 条
  • [1] Online streaming feature selection based on neighborhood rough set
    Li, Shuangjie
    Zhang, Kaixiang
    Li, Yali
    Wang, Shuqin
    Zhang, Shaoqiang
    [J]. APPLIED SOFT COMPUTING, 2021, 113
  • [2] Label distribution feature selection based on neighborhood rough set
    Wu, Yilin
    Guo, Wenzhong
    Lin, Yaojin
    [J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2024, 36 (23):
  • [3] Neighborhood rough set based heterogeneous feature subset selection
    Hu, Qinghua
    Yu, Daren
    Liu, Jinfu
    Wu, Congxin
    [J]. INFORMATION SCIENCES, 2008, 178 (18) : 3577 - 3594
  • [4] Neighborhood rough set with neighborhood equivalence relation for feature selection
    Shangzhi Wu
    Litai Wang
    Shuyue Ge
    Zhengwei Hao
    Yulin Liu
    [J]. Knowledge and Information Systems, 2024, 66 : 1833 - 1859
  • [5] Neighborhood rough set with neighborhood equivalence relation for feature selection
    Wu, Shangzhi
    Wang, Litai
    Ge, Shuyue
    Hao, Zhengwei
    Liu, Yulin
    [J]. KNOWLEDGE AND INFORMATION SYSTEMS, 2024, 66 (03) : 1833 - 1859
  • [6] Supervised spectral feature selection with neighborhood rough set
    Liu, Qiong
    Cai, Mingjie
    Li, Qingguo
    [J]. APPLIED SOFT COMPUTING, 2024, 165
  • [7] Fault feature subset selection based on rough set theory
    Zhao, Yueling
    Xu, Lin
    Wang, Jianhui
    Gu, Shusheng
    [J]. Complexity Analysis and Control for Social, Economical and Biological Systems, 2006, 1 : 162 - 171
  • [8] An Exact Feature Selection Algorithm Based on Rough Set Theory
    Rezvan, Mohammad Taghi
    Hamadani, Ali Zeinal
    Hejazi, Seyed Reza
    [J]. COMPLEXITY, 2015, 20 (05) : 50 - 62
  • [9] A Novel Algorithm for Feature Selection Based on Rough set Theory
    Zhou Feng-xiang
    Mu Chun-ge
    Xu Qun-san
    Zhang Xiao-feng
    [J]. 2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11, 2008, : 800 - +
  • [10] Information and Rough Set Theory Based Feature Selection Techniques
    Cervante, Liam
    Gao, Xiaoying
    [J]. ACTIVE MEDIA TECHNOLOGY, AMT 2013, 2013, 8210 : 166 - 176
12345