计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 168-173.doi: 10.11896/jsjkx.210500067
王志成, 高灿, 邢金明
WANG Zhi-cheng, GAO Can, XING Jin-ming
摘要: 属性约简是三支决策理论的重要研究内容之一。然而,现有基于三支决策的属性约简方法过于严格,限制了其属性约简的效率。文中提出了一种基于正域的三支近似属性约简方法。具体地,属性约简被视为根据条件属性与决策属性的相关性,将所有属性划分为正域、负域或边界域3类的过程。首先通过保留正域度量来去除负域属性,然后通过放松正域度量来迭代地排除一些边界属性,最后将剩余属性构成一个近似约简。UCI数据实验结果显示,与其他代表性的方法相比,所提方法能在保持甚至提升性能的同时获得更小的属性约简,说明了所提方法的有效性。
中图分类号:
[1] LI Y,LI T,LIU H.Recent advances in feature selection and its applications[J].Knowledge and Information Systems,2017,53(3):551-577. [2] BISHOP C M.Pattern recognition and machine learning[M].New York:Springer,2006. [3] ARMANFARD N,REILLY J P,KOMEILI M.Local feature selection for data classification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,38(6):1217-1227. [4] MIAO D Q,ZHAO Y,YAO Y Y,et al.Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model[J].Information Sciences,2009,179(24):4140-4150. [5] LI F,MIAO D Q,PEDRYCZ W.Granular multi-label feature selection based on mutual information[J].Pattern Recognition,2017,67:410-423. [6] YAO Y Y,ZHAO Y.Discernibility matrix simplification forconstructing attribute reducts[J].Information Sciences,2009,179(7):867-882. [7] LAI Z H,YONG X,JIAN Y,et al.Rotational invariant dimensionality reduction algorithms[J].IEEE Transactions on Cybernetics,2016,47(11):3733-3746. [8] WANG X,PENG Z H,LI J Y,et al.Method of Concept Reduction Based on Concept Discernibility Matrix[J].Computer Science,2021,48(1):125-130. [9] ZENG H K,MI J S,LI Z L.Dynamic Updating Method of Concepts and Reduction in Formal Context[J].Computer Science,2021,48(1):131-135. [10] PAWLAK Z.Rough sets[J].International Journal of Computer &Information Sciences,1982,11(5):341-356. [11] YAO Y Y.Three-way decisions with probabilistic rough sets[J].Information Sciences,2010,180(3):341-353. [12] YAO Y Y.Three-way decision and granular computing[J].International Journal of Approximate Reasoning,2018,103:107-123. [13] YAO Y Y.Three-way granular computing,rough sets,and formal concept analysis[J].International Journal of Approximate Reasoning,2020,116:106-125. [14] YAO Y Y,ZHAO Y.Attribute reduction in decision-theoreticrough set models[J].Information Sciences,2008,178(17):3356-3373. [15] YAO Y Y.Three-way conflict analysis:Reformulations and extensions of the Pawlak model[J].Knowledge-Based Systems,2019,180:26-37. [16] YAO Y Y.Three-way decisions and cognitive computing[J].Cognitive Computation,2016,8(4):543-554. [17] ZHAO Y,WONG S K M,YAO Y Y.A note on attribute reduction in the decision-theoretic rough set model[C]//Transactions on Rough Sets XIII.Berlin:Springer,2011:260-275. [18] LI H X,ZHOU X Z,ZHAO J B,et al.Non-monotonic attribute reduction in decision-theoretic rough sets[J].Fundamenta Informaticae,2013,126(4):415-432. [19] MA X A,WANG G Y,YU H.Heuristic method to attribute reduction for decision region distribution preservation[J].Ruan Jian Xue Bao/Journal of Software,2014,25(8):1761-1780. [20] MA X A,WANG G Y,YU H,et al.Decision region distribution preservation reduction in decision-theoretic rough set model[J].Information Sciences,2014,278:614-640. [21] GAO C,LAI Z H,ZHOU J,et al.Maximum decision entropy-based attribute reduction in decision-theoretic rough set model[J].Knowledge-Based Systems,2018,143:179-191. [22] ZHANG X Y,MIAO D Q.Region-based quantitative and hierarchical attribute reduction in the two-category decision theoretic rough set model[J].Knowledge-Based Systems,2014,71:146-161. [23] ZHANG X Y,MIAO D Q.Reduction target structure-based hie-rarchical attribute reduction for two-category decision-theoretic rough sets[J].Information Sciences,2014,277:755-776. [24] JIA X Y,LIAO W H,TANG Z M,et al.Minimum cost attribute reduction in decision-theoretic rough set models[J].Information Sciences,2013,219:151-167. [25] JIA X Y,TANG Z M,LIAO W H,et al.On an optimization representation of decision-theoretic rough set model[J].International Journal of Approximate Reasoning,2014,55(1):156-166. [26] LIAO S J,ZHU Q X,FAN M.Cost-sensitive attribute reduction in decision-theoretic rough set models[J].Mathematical Problems in Engineering,2014,35(1):1-9. [27] SLEZAK D.Approximate reducts in decision tables[C]//Proceedings of IPMU’ 96.Granada:Spain,1996:1159-1164. [28] SLEZAK D.Approximate entropy reducts[J].FundamentalInformaticae,2002,53(3/4):365-390. [29] YANG M.Approximate reduction based on conditional information entropy in decision tables[J].Acta Electronica Sinica,2007,35(11):2156-2160. [30] YANG X,LI T R,LIU D,et al.A unified framework of dynamic three-way probabilistic rough sets[J].Information Sciences,2017,420:126-147. [31] ZHANG Q H,XIE Q,WANG G Y.A survey on rough set theory and its applications[J].CAAI Transactions on Intelligence Technology,2016,1(4):323-333. [32] YAO Y Y,WONG S K M.A decision theoretic framework for approximating concepts[J].International Journal of Man-machine Studies,1992,37(6):793-809. [33] LICHMAN M.UCI machine learning repository [DB/OL].University of California,Irvine,CA,USA,2013.http://archive.ics.uci.edu/ml. [34] PEDREGOSA F,VAROQUAUX G,GRAMFORT A,et al.Scikit-learn:Machine learning in Python[J].Journal of Machine Learning Research,2011,12:2825-2830. |
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