计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 168-173.doi: 10.11896/jsjkx.210500067

• 数据库&大数据&数据科学 • 上一篇    下一篇

一种基于正域的三支近似约简

王志成, 高灿, 邢金明   

  1. 深圳大学计算机与软件学院 广东 深圳 518060; 深圳大学智能信息处理重点实验室 广东 深圳 518060
  • 收稿日期:2021-05-10 修回日期:2021-10-15 发布日期:2022-04-01
  • 通讯作者: 高灿(2005gaocan@163.com)
  • 作者简介:(wzc2802005420@163.com)
  • 基金资助:
    国家自然科学基金(61806127,62076164); 佛山市教育局项目(2019XJZZ05)

Three-way Approximate Reduction Based on Positive Region

WANG Zhi-cheng, GAO Can, XING Jin-ming   

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, ChinaKey Laboratory of Intelligent Information Processing, Shenzhen, Guangdong 518060, China
  • Received:2021-05-10 Revised:2021-10-15 Published:2022-04-01
  • About author:WANG Zhi-cheng,born in 1998,postgraduate.His main research interests include machine learning and granular computing.GAO Can,born in 1983,Ph.D,assistant professor,master supervisor.His main research interests include machine lear-ning and computer vision.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61806127,62076164) and Bureau of Education of Foshan(2019XJZZ05).

摘要: 属性约简是三支决策理论的重要研究内容之一。然而,现有基于三支决策的属性约简方法过于严格,限制了其属性约简的效率。文中提出了一种基于正域的三支近似属性约简方法。具体地,属性约简被视为根据条件属性与决策属性的相关性,将所有属性划分为正域、负域或边界域3类的过程。首先通过保留正域度量来去除负域属性,然后通过放松正域度量来迭代地排除一些边界属性,最后将剩余属性构成一个近似约简。UCI数据实验结果显示,与其他代表性的方法相比,所提方法能在保持甚至提升性能的同时获得更小的属性约简,说明了所提方法的有效性。

关键词: 粗糙集, 近似约简, 三支决策, 正域, 属性约简

Abstract: Attribute reduction is one of the most important research topics in the theory of three-way decision.However, the existing attribute reduction methods based on three-way decision are too strict, which limit the efficiency of attribute reduction.In this paper, a three-way approximate attribute reduction method based on the positive region is proposed.More specifically, attri-bute reduction is considered as the process of determining attributes as positive, boundary, or negative ones according to their correlation to the decision attribute.The negative attributes are first removed by retaining the measure of the positive region.Then, some of the boundary attributes are iteratively excluded by relaxing the positive region measure.Finally, an approximate reduction is formed by the remaining attributes.Extensive experiments on UCI data sets demonstrate that the proposed method can achieve much smaller reducts with the same or even better performance in comparison with other representative methods, showing the effectiveness in attribute reduction.

Key words: Approximate reduction, Attributes reduction, Positive region, Rough set, Three-way decision

中图分类号: 

  • TP391
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