计算机工程 ›› 2019, Vol. 45 ›› Issue (9): 211-215.doi: 10.19678/j.issn.1000-3428.0053470

• 人工智能及识别技术 • 上一篇    下一篇

权重模糊粗糙集的分类规则挖掘算法

李抒音1, 刘洋2   

  1. 1. 郑州航空工业管理学院 艺术设计学院, 郑州 450046;
    2. 郑州大学 信息工程学院, 郑州 450001
  • 收稿日期:2018-12-24 修回日期:2019-03-19 出版日期:2019-09-15 发布日期:2019-09-03
  • 作者简介:李抒音(1982-),女,讲师、硕士,主研方向为数据挖掘、智能计算;刘洋,讲师、博士。
  • 基金项目:
    国家自然科学基金(61303044,61572444)。

Classification Rule Mining Algorithm for Weighted Fuzzy Rough Sets

LI Shuyin1, LIU Yang2   

  1. 1. School of Art and Design, Zhengzhou University of Aeronautics, Zhengzhou 450046, China;
    2. School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
  • Received:2018-12-24 Revised:2019-03-19 Online:2019-09-15 Published:2019-09-03

摘要: 针对粗糙集分类规则挖掘算法LEM2剪枝条件过于严格的问题,提出一种权重模糊粗糙集的改进规则挖掘算法。在用例带权重的模糊粗糙集理论框架上分析面向混合数据的分类规则挖掘算法,引入粗糙集模型的近似覆盖参数作为挖掘算法的泛化度量参数,实现对规则集数量和规则形式复杂程度的调节。实验结果表明,与LEM2算法和DataSqueezer算法相比,该算法的平均精度和平均召回率更优,分别为81%和80%,且生成规则的平均长度最短。

关键词: 知识发现, 分类, 粗糙集理论, 规则挖掘, 权重学习

Abstract: Aiming at the problem that the pruning condition of the rough set classification rule mining algorithm LEM2 is too strict,this paper proposes an improved rule mining algorithm for weighted fuzzy rough sets.In the framework of fuzzy rough set theory with weights of use cases,the mining method of classification rules for mixed data is discussed.The approximate covering parameters of rough set model are introduced as the generalization metric parameters of mining algorithm,which realizes the adjustment of the number of rule sets and the complexity of the rule form.Experimental results show that compared with LEM2 and DataSqueezer algorithms,the average precision and average recall of the proposed algorithm are better,reaching 81% and 80% respectively,and the average length of the generation rule is the shortest.

Key words: knowledge discovery, classification, rough set theory, rule mining, weighted learning

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