计算机科学 ›› 2020, Vol. 47 ›› Issue (3): 67-72.doi: 10.11896/jsjkx.190100196
陈毅宁1,陈红梅2
CHEN Yi-ning1,CHEN Hong-mei2
摘要: 属性约简能有效地去除不必要属性,提高分类器的性能。模糊粗糙集是处理不确定信息的重要范式,能有效地应用于属性约简。在模糊粗糙集中,样本分布的不确定性会影响对象的近似集,进而影响有效属性约简的获取。为有效地定义近似集,文中提出了基于距离比值尺度的模糊粗糙集,该模型引入了基于距离比值尺度的样本集的定义,通过对距离比值尺度的控制,避免了样本分布不确定性对近似集的影响;给出了该模型的基本性质,定义了新的依赖度函数,进而设计了属性约简算法;以SVM,NaiveBayes和J48作为测试分类器,在UCI数据集上评测所提算法的性能。实验结果表明,所提出的属性约简算法能够有效获取约简并提高分类的精度。
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[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. |
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