模式识别与人工智能
2025年1月11日 星期六   首 页     期刊简介     编委会     投稿指南     伦理声明     联系我们                                                                English
模式识别与人工智能  2018, Vol. 31 Issue (3): 230-235    DOI: 10.16451/j.cnki.issn1003-6059.201803004
论文与报告 最新目录| 下期目录| 过刊浏览| 高级检索 |
全粒度粗糙集属性约简
邓大勇1
1.浙江师范大学 行知学院 金华 321004
Attribute Reduction for Entire-Granulation Rough Sets
DENG Dayong1
1.Xingzhi College, Zhejiang Normal University, Jinhua 321004

全文: PDF (1335 KB)   HTML (1 KB) 
输出: BibTeX | EndNote (RIS)      
摘要 全粒度粗糙集是一种动静结合的粗糙集模型,在一定程度上可以表示人类认识的复杂性、多样性和不确定性.文中定义概念的全粒度属性约简,完善全粒度粗糙集属性约简的定义.探索概念的全粒度属性约简、全粒度绝对约简及全粒度Pawlak约简的性质,指明这些属性约简之间的关系,有助于全粒度属性约简的实际应用及启发式算法的产生.
服务
把本文推荐给朋友
加入我的书架
加入引用管理器
E-mail Alert
RSS
作者相关文章
邓大勇
关键词 全粒度粗糙集概念的全粒度属性约简全粒度绝对约简全粒度Pawlak约简属性约简    
Abstract:Entire-granulation rough sets is a kind of dynamic and static combining rough set model. They can partly express complexity,diversity and uncertainty of human cognition. The entire-granulation attribute reducts for a single concept are defined and the definitions of attribute reducts for entire-granulation rough sets are completed. The properties of entire-granulation attribute reducts, including entire-granulation attribute reducts for a single concept, entire-granulation absolute attribute reducts and entire-granulation Pawlak reducts, are investigated.Moreover, the relationships among various kinds of entire-granulation attribute reducts are studied. The obtained results contribute to practical applications and the generation of heuristic algorithms of entire-granulation attribute reducts.
Key wordsEntire-Granulation Rough Sets    Entire-Granulation Attribute Reducts for a Single Concept    Entire-Granulation Absolute Reducts    Entire-Granulation Pawlak Reducts    Attribute Reducts   
收稿日期: 2017-09-18     
ZTFLH: TP 18  
基金资助:浙江省自然科学基金项目(No.LY15F020012)资助
作者简介: 邓大勇,博士,副教授,主要研究方向为粗糙集、粒计算、数据挖掘.E-mail:dayongd@163.com.
引用本文:   
邓大勇. 全粒度粗糙集属性约简[J]. 模式识别与人工智能, 2018, 31(3): 230-235. DENG Dayong. Attribute Reduction for Entire-Granulation Rough Sets. , 2018, 31(3): 230-235.
链接本文:  
http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201803004      或     http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I3/230
版权所有 © 《模式识别与人工智能》编辑部
地址:安微省合肥市蜀山湖路350号 电话:0551-65591176 传真:0551-65591176 Email:bjb@iim.ac.cn
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn