计算机科学 ›› 2018, Vol. 45 ›› Issue (1): 133-139.doi: 10.11896/j.issn.1002-137X.2018.01.022

• 第十六届中国机器学习会议 • 上一篇    下一篇

加权模糊粗糙约简

范星奇,李雪峰,赵素云,陈红,李翠平   

  1. 中国人民大学信息学院 北京100872,中国人民大学环境学院 北京100872,中国人民大学信息学院 北京100872,中国人民大学信息学院 北京100872,中国人民大学信息学院 北京100872
  • 出版日期:2018-01-15 发布日期:2018-11-13

Weighted Attribute Reduction Based on Fuzzy Rough Sets

FAN Xing-qi, LI Xue-feng, ZHAO Su-yun, CHEN Hong and LI Cui-ping   

  • Online:2018-01-15 Published:2018-11-13

摘要: 基于模糊粗糙集的传统约简算法的时间代价较高,在处理大规模数据时耗时过长,且在许多实际大规模数据集上存在有限时间内无法收敛等问题。因此将权重引入属性约简的定义中,其中属性权重是属性重要度的数值指标。通过构建优化问题来求解属性权重,证明了属性依赖度即是属性权重的最优解。因此,提出了基于属性权重排序的约简算法,从而大大提升了约简的速度,使得约简算法可以应用于大规模数据集,特别是高维数据集中。

关键词: 模糊粗糙集,属性约简,权重,高维数据

Abstract: Now the existing classical reduction algorithms have high time consumption,especially on the large scale datasets.To handle this problem,this paper introduced weights into the concept of attribute reduction,where weight is the measure of attribute significance.By building optimization problem about weights,it is fond that the attribute dependency degree is just the optimal solution of the weights.As a result,this paper proposed a reduction algorithm based on ranked weights,which significantly accelerate attribute reduction.Numerical experiments demonstrate that the proposed algorithm is suitable on large scale datasets,especially on the datasets with high dimension.

Key words: Fuzzy rough sets,Attribute reduction,Weights,High dimension datasets

[1] HU J.Survey on feature dimension reduction for high-dimen-sional data[J].Application Research of Computers,2008(9):2601-2606.(in Chinese) 胡洁.高维数据特征降维研究综述[J].计算机应用研究,2008(9):2601-2606.
[2] JENSON R,SHEN Q.Fuzzy-rough attribute reduction with application to web categorization[J].Fuzzy Sets and Systems,2004,141(3):469-485.
[3] JENSON R,SHEN Q.Fuzzy-rough sets for descriptive dimensionality reduction[C]∥IEEE International Comference on Plasma Science.2002:29-34.
[4] MENGER K.Statistical metrics[J].Proceedings of the National Academy of Sciences of the United States of America,1942,28(12):535-537.
[5] DUBOIS D,PRADE H.Fuzzy sets in approximate reasoning,Part 1:Inference with possibility distributions[J].Fuzzy Sets & Systems,1999,40(1):73-132.
[6] ZHAO Y L,WANG Y L.Generalized fuzzy rough set approach to fuzzy information reduct[J].Computer Engineering and Applications,2008,44(4):169-171.(in Chinese) 赵越岭,王英丽.广义模糊粗糙集在模糊信息约简中的应用[J].计算机工程与应用,2008,44(4):169-171.
[7] 胡清华,于达仁.应用粗糙计算[M].北京:科学出版社,2012.
[8] ZHAO S Y,TSANG C C,CHEN D G.The model of fuzzy variable precision rough sets[J].IEEE Trans.Fuzzy Systems,2009,17(2):451-467.
[9] ZHAO S Y,TSANG C C.On Fuzzy approximation Operators in Attribute Reduction with Fuzzy Rough Sets[J].Information Sciences,2008,178(16):3162-3176.
[10] YAO Y Y,ZHAO Y.Attribute reduction in decision-theoreticrough set models[J].Information Sciences,2008,178(17):3356-3373.
[11] UCI.Machine Learning Repository.http://archive.ics.uci.edu/ml.
[12] JMMM C.A New Minkowski Distance Based on Induced Aggregation Operators[J].International Journal of Computational Intelligence Systems,2011(2):123-133.
[13] ZHANG Z X,FAN X Q,ZHAO S Y,et al.Fast reduction algorithm research based on k-nearest neighbor fuzzy rough set[J].Journal of Frontiers of Computer Science and Technology,2015,9(1):14-23.(in Chinese) 张照星,范星奇,赵素云,等.k-近邻模糊粗糙集的快速约简算法研究[J].计算机科学与探索,2015,9(1):14-23.
[14] TSANG E C C,CHEN D G,YEUNG D S,et al.Attributes reduction using fuzzy rough sets[J].IEEE Transactions on fuzzy system,2008,16(5):1130-1141.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75 .
[2] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[3] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[4] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[5] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99 .
[6] 周燕萍,业巧林. 基于L1-范数距离的最小二乘对支持向量机[J]. 计算机科学, 2018, 45(4): 100 -105 .
[7] 刘博艺,唐湘滟,程杰仁. 基于多生长时期模板匹配的玉米螟识别方法[J]. 计算机科学, 2018, 45(4): 106 -111 .
[8] 耿海军,施新刚,王之梁,尹霞,尹少平. 基于有向无环图的互联网域内节能路由算法[J]. 计算机科学, 2018, 45(4): 112 -116 .
[9] 崔琼,李建华,王宏,南明莉. 基于节点修复的网络化指挥信息系统弹性分析模型[J]. 计算机科学, 2018, 45(4): 117 -121 .
[10] 王振朝,侯欢欢,连蕊. 抑制CMT中乱序程度的路径优化方案[J]. 计算机科学, 2018, 45(4): 122 -125 .