Computer Science ›› 2020, Vol. 47 ›› Issue (3): 67-72.doi: 10.11896/jsjkx.190100196

• Database & Big Data & Data Science • Previous Articles     Next Articles

Attribute Reduction of Fuzzy Rough Set Based on Distance Ratio Scale

CHEN Yi-ning1,CHEN Hong-mei2   

  1. (School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China)1;
    (Key Laboratory of Cloud Computing and Intelligent Technology, Southwest Jiaotong University, Chengdu 611756, China)2
  • Received:2019-01-23 Online:2020-03-15 Published:2020-03-30
  • About author:CHEN Yi-ning,born in 1995,postgraduate.His main research interests include areas of rough set and so on. CHEN Hong-mei,born in 1971,Ph.D,professor,Ph.D supervisor,is member of China Computer Federation (CCF).Her main research interests include rough set,granular computing,and intelligent information processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61572406).

Abstract: Attribute reduction can effectively remove the unnecessary attributes in order to improve the performance of the classifiers.Fuzzy rough set theory is an important formal of processing the uncertain information.In the fuzzy rough set model,the approximations of an object may be affected by uncertain distribution of samples.Consequently,acquiring effective attribute reduction may be influenced.In order to effectively define approximations,this paper proposed a novel fuzzy rough set model named distance ratio scale based fuzzy rough set.The definition of samples based on distance ratio scale is introduced.The influence of uncertain distribution of samples to approximations is avoided by controlling the distance ratio scale.The basic properties of this fuzzy rough set model are presented and the new dependent function is defined.Furthermore,the algorithm for attribute reduction is designed.SVM,NaiveBayes,and J48 were used as test classifier executed on UCI data sets to verify the performance of the proposed algorithm.The experimental results show that attribute reduction can be effectively obtained by the proposed attribute reduction algorithm and the classification precisions of classifiers are improved.

Key words: Attribute reduction, Distance ratio scale, Fuzzy rough set

CLC Number: 

  • TP301.6
[1]HONG R C,PAN J X,HAO S J,et al.Image quality assessment based on matching pursuit[J].Information Sciences,2014,273:196-211.
[2]HONG R C,WANG M,GAO Y,et al.Image annotation by mul- tiple-instance learning with discriminative feature mapping and selection[J].IEEE Transactions on Cybernetics,2014,44(5):669-680.
[3]LU J J,ZHAO T Z,ZHANG Y F.Feature selection based-on genetic algorithm for image annotation[J].Knowledge-Based Systems,2008,21(8):887-891.
[4]PAWLAK Z.Rough set[J].International Journal of Computer & Information Sciences,1982,11(5):341-356.
[5]CHEN J K,LI J J,LIN Y J.Computing connected components of simple undirected graphs based on generalized rough sets[J].Knowledge-Based Systems,2013,37:80-85.
[6]CHEN H M,LI T R,LUO C,et al.A decision-theoretic rough set approach for dynamic data mining[J].IEEE Transactions on Fuzzy Systems,2015,23(6):1958-1970.
[7]CHEN J K,LIN Y,LIN G,et al.The relationship between attribute reducts in rough sets and minimal vertex covers of graphs[J].Information Sciences,2015,325:87-97.
[8]LI J H,REN Y,MEI C L,et al.A comparative study of multigranulation rough sets and concept lattices via rule acquisition[J].Knowledge-Based Systems,2016,91:152-164.
[9]DUBOIS D,PRADE H.Rough fuzzy sets and fuzzy rough sets[J].International Journal of General Systems,1990,17(2/3):191-209.
[10]HU Q H,YU D R,LIU J F,et al.Neighborhood rough set based heterogeneous feature subset selection[J].Information Sciences,2008,178(18):3577-3594.
[11]JENSEN R,SHEN Q.Fuzzy-rough attribute reduction with application to web categorization[J].Fuzzy Sets and Systems,2004,141(3):469-485.
[12]SHEN Q,JENSEN R.Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring[J].Pattern Recognition,2004,37(7):1351-1363.
[13]HU Q H,YU D R,XIE Z X.Information-preserving hybrid data reduction based on fuzzy-rough techniques[J].Pattern Recognition Letters,2006,27(5):414-423.
[14]WANG C Z,QI Y L,HE Q.Attribute reduction using distance-based fuzzy rough sets[C]∥International Conference on Machine Learning and Cybernetics.IEEE,2015:860-865.
[15]PAWLAK Z.Rough Sets:Theoretical Aspects of Reasoning about Data[M].Kluwer Academic Publishers,1992.
[16]ZHANG W X.Rough set theory and method[M].Beijing:Science Press,2001.
[17]YEUNG D S,CHEN D G,TSANG E C C,et al.On the generalization of fuzzy rough sets[J].IEEE Transactions on Fuzzy Systems,2005,13(3):343-361.
[18]MORSI N N,YAKOUT M M.Axiomatics for fuzzy rough sets[J].Fuzzy Sets and Systems,1998,100(1/2/3):327-342.
[19]CORTES C,VAPNIK V.Support-vector networks[J].Machine Learning,1995,20(3):273-297.
[20]HU Q H,ZHANG L,CHEN D G,et al.Gaussian kernel based fuzzy rough sets:Model,uncertainty measures and applications[J].International Journal of Approximate Reasoning,2010,51(4):453-471.
[1] XU Si-yu, QIN Ke-yun. Topological Properties of Fuzzy Rough Sets Based on Residuated Lattices [J]. Computer Science, 2022, 49(6A): 140-143.
[2] WANG Zi-yin, LI Lei-jun, MI Ju-sheng, LI Mei-zheng, XIE Bin. Attribute Reduction of Variable Precision Fuzzy Rough Set Based on Misclassification Cost [J]. Computer Science, 2022, 49(4): 161-167.
[3] LI Yan, FAN Bin, GUO Jie, LIN Zi-yuan, ZHAO Zhao. Attribute Reduction Method Based on k-prototypes Clustering and Rough Sets [J]. Computer Science, 2021, 48(6A): 342-348.
[4] ZENG Hui-kun, MI Ju-sheng, LI Zhong-ling. Dynamic Updating Method of Concepts and Reduction in Formal Context [J]. Computer Science, 2021, 48(1): 131-135.
[5] SANG Bin-bin, YANG Liu-zhong, CHEN Hong-mei, WANG Sheng-wu. Incremental Attribute Reduction Algorithm in Dominance-based Rough Set [J]. Computer Science, 2020, 47(8): 137-143.
[6] YUE Xiao-wei, PENG Sha and QIN Ke-yun. Attribute Reduction Methods of Formal Context Based on ObJect (Attribute) Oriented Concept Lattice [J]. Computer Science, 2020, 47(6A): 436-439.
[7] XU Yi,TANG Jing-xin. Attribute Reduction Algorithm Based on Optimized Discernibility Matrix and Improving Discernibility Information Tree [J]. Computer Science, 2020, 47(3): 73-78.
[8] HOU Cheng-jun,MI Ju-sheng,LIANG Mei-she. Attribute Reduction Based on Local Adjustable Multi-granulation Rough Set [J]. Computer Science, 2020, 47(3): 87-91.
[9] YANG Wen-jing,ZHANG Nan,TONG Xiang-rong,DU Zhen-bin. Class-specific Distribution Preservation Reduction in Interval-valued Decision Systems [J]. Computer Science, 2020, 47(3): 92-97.
[10] GUO Qing-chun,MA Jian-min. Judgment Methods of Interval-set Consistent Sets of Dual Interval-set Concept Lattices [J]. Computer Science, 2020, 47(3): 98-102.
[11] LONG Bing-han, XU Wei-hua, ZHANG Xiao-yan. Distribution Attribute Reduction Based on Improved Discernibility Information Tree in Inconsistent System [J]. Computer Science, 2019, 46(6A): 115-119.
[12] LI Yan, ZHANG Li, CHEN Jun-fen. Attribute Reduction Method Based on Sequential Three-way Decisions in Dynamic Information Systems [J]. Computer Science, 2019, 46(6A): 120-123.
[13] LI Yan, ZHANG Li, WANG Xue-jing, CHEN Jun-fen. Attribute Reduction for Sequential Three-way Decisions Under Dominance-Equivalence Relations [J]. Computer Science, 2019, 46(2): 242-148.
[14] JIANG Ze-hua, WANG Yi-bo, XU Gang, YANG Xi-bei, WANG Ping-xin. Multi-scale Based Accelerator for Attribute Reduction [J]. Computer Science, 2019, 46(12): 250-256.
[15] LI Zhong-ling, MI Ju-sheng, XIE Bin. Attribute Reduction in Inconsistent Decision Formal Contexts [J]. Computer Science, 2019, 46(12): 257-260.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!