计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 186-190.doi: 10.11896/j.issn.1002-137X.2018.08.033

• 人工智能 • 上一篇    下一篇

监督邻域粗糙集

汪琳娜1,2, 杨新3, 杨习贝4   

  1. 四川工商学院电子信息工程学院 成都 6117451
    里贾纳大学计算机科学学院 萨斯喀彻温 里贾纳S4S 0A22
    西南交通大学信息科学与技术学院 成都 6117563
    江苏科技大学计算机科学与工程学院 江苏 镇江2120034
  • 收稿日期:2017-07-21 出版日期:2018-08-29 发布日期:2018-08-29
  • 作者简介:汪琳娜(1986-),女,硕士,讲师,主要研究方向为粒计算、粗糙集和三支决策等; 杨 新(1981-),男,博士生,副教授,主要研究方向为粒计算、粗糙集和三支决策等; 杨习贝(1980-),男,博士后,副教授,主要研究方向为粗糙集理论、粒计算与机器学习,E-mail:zhenjiangyangxibei@163.com(通信作者)。
  • 基金资助:
    本文受国家自然科学基金项目(61572242,61573292,71571148),四川省教育厅自然科学基金项目(18ZB0373)资助。

Supervised Neighborhood Rough Set

WANG Lin-na1,2, YANG Xin3, YANG Xi-bei4   

  1. School of Electronic and Information Engineering,Sichuan Technology and Business University,Chengdu 611745,China1
    Department of Computer Science,University of Regina,Regina,Saskatchewan S4S 0A2,Canada2
    School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China3
    School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China4
  • Received:2017-07-21 Online:2018-08-29 Published:2018-08-29

摘要: 传统单一阈值的邻域粗糙集不能有效降低信息的不确定性。考虑对象已有或预测的类别标签信息,通过引入类内和类间两种阈值,提出一种新的邻域粒化方法,并构建了一种基于监督邻域的粗糙集模型。该模型是传统邻域粗糙集的推广形式。通过分析双阈值下的邻域粒子变化规律,给出该模型的粗糙近似质量和条件熵单调性变化定理。最后通过4个UCI数据集验证了该模型的性能。实验结果显示,可以通过调节监督阈值参数来改善论域的邻域粒化效果,并降低信息的不确定性。

关键词: 不确定性, 监督邻域, 邻域粒化, 双阈值

Abstract: The uncertainty of information can’t be efficiently reduced by traditional neighborhood rough set with single threshold.By considering the existing or predicted category label information of the object,this paper introduced two kinds of thresholds,namely,intra-class and inter-class,and proposed a novel neighborhood granulation methods to construct a rough set model based on supervised neighborhood.This model is the generalized form of conventional neighborhood rough set.Moreover,the theorem of monotonic variation with approximate quality and conditional entropy was presented through analyzing the change rules of neighborhood particlesunder double thresholds.Finally,the performance of the model was demonstrated on four data sets of UCI.The results show that the effect of neighborhood granulation can be improved andthe uncertainty of information can be reduced by adjusting supervised threshold parameters.

Key words: Double thresholds, Neighborhood granulation, Supervised neighborhood, Uncertainty

中图分类号: 

  • TP181
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