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Tytuł artykułu

A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Attribute reduction is an important issue in rough set theory and has already been studied from the algebra viewpoint and information viewpoint of rough set theory respectively. However, the concepts of attribute reduction based on these two different viewpoints are not equivalent to each other. In this paper, we make a comparative study on the quantitative relationship between some basic concepts of rough set theory like attribute reduction, attribute significance and core defined from these two viewpoints. The results show that the relationship between these conceptions from the two viewpoints is rather an inclusion than an equivalence due to the fact that the rough set theory discussed from the information point of view restricts attributes and decision tables more specifically than it does when considered from the algebra point of view. The identity of the two viewpoints will hold in consistent information decision tables only. That is, the algebra viewpoint and information viewpoint are equivalent for a consistent decision table, while different for an inconsistent decision table. The results are significant for the design and development of methods for information reduction.
Wydawca
Rocznik
Strony
289--301
Opis fizyczny
Bibliogr. 21 poz.
Twórcy
autor
  • Institute of Computer Science and Technology, Chongqing University of Postas and Telecommunications, Chongqing, 400065, China
autor
  • Institute of Computer Science and Technology, Chongqing University of Postas and Telecommunications, Chongqing, 400065, China
autor
  • Institute of Computer Science and Technology, Chongqing University of Postas and Telecommunications, Chongqing, 400065, China
autor
  • Institute of Computer Science and Technology, Chongqing University of Postas and Telecommunications, Chongqing, 400065, China
Bibliografia
  • [1] Pawlak, Z.: Rough sets. Int. Journal of Computer and Information Science 11(5)(1982), 341-356.
  • [2] Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer, 1991.
  • [3] Wang, G.Y.: Rough Set Theory and Knowledge Acquisition. Xi’an Jiaotong University Press, Xi’an, 2001.
  • [4] Miao, D.Q., Wang, J.: An information representation of the concepts and operations in rough set theory. Chinese Journal of Software 10(2)(1999), 113-116.
  • [5] Wang, G.Y.: Algebra View and Information View of Rough Sets Theory, in Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, Belur V. Dasarathy, Editor, Proceedings of SPIE Vol. 4384, 2001, 200-207.
  • [6] Chen, X.H., Zhu, S.J., Ji, Y.D.: Entropy Based Uncertainty Measures for Classification Rules with Inconsistency Tolerance. Systems, Man, and Cybernetics, 2000 IEEE International Conference on, 8-11 Oct., Vol.4, 2000, 2816-2821.
  • [7] Jelonek, J., et al: Rough set reduction of attributes and their domains for neural networks. International Journal of Computational Intelligence 11(2)(1995), 338-347.
  • [8] Ye, D.Y., et al.: An improvement on the Jelonek’s algorithm of attribute reduction. Acta Electronic Sinica 12(2000), 81-82.
  • [9] Chang, L.Y., Wang, G.Y., Wu, Y.: An approach for attribute reduction and rule generation based on rough set theory. Chinese Journal of Software 10(11)(1999), 1206-1211.
  • [10] Miao, D.Q., Hu, G.R.: A heuristic algorithm for reduction of knowledge. Chinese Journal of Computer Research and Development 36(6)(1999), 681-684.
  • [11] Wang, G.Y., Yu, H., Yang, D.C., Wu, Z.F.: Knowledge Reduction Based on Rough Set and Information Entropy. Proc. the World Multi-conference on Systemics, Cybernetics and Informatics, Orlando, Florida, 2001, 555-560.
  • [12] Nguyen, S.H., Nguyen, H.S.: Some Efficient Algorithms for Rough Set Methods. Proc. the Conf. on Information Processing and Management of Uncertainty in Knowledge Based Systems, Granada, Spain, 1996, 1451-1456.
  • [13] Han, B., Wu, T.J.: Information Entropy Based Reduct Searching Algorithm. American Control Conference, 2002. Proceedings of the 2002, 8-10 May, Vol.6, 2002, 4577-4582.
  • [14] Yang, J., Wang, H., Hu, X.G., Hu, Z.H.: A New Classification Algorithm Based on Rough Set and Entropy. Machine Learning and Cybernetics, 2003 International Conference on, 2-5 Nov., Vol.1, 2003, 364-367.
  • [15] Bai, J.S., Fan, B., Xue, J.Y.: Knowledge Representation and Acquisition Approach Based on Decision Tree. Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003 International Conference on, 26-29 Oct. 2003, 533-538.
  • [16] Wang, G.Y.: Relationship between the Algebra View and Information View of Rough Set, in Data Mining and Knowledge Discovery: Theory, Tools, and Technology V, Belur V. Dasarathy, Editor, Proceedings of SPIE, Vol.5098, 2003, 103-113.
  • [17] Wang, G.Y.: Attribute core of inconsistent decision information systems. Journal of Shanghai Jiaotong University 38(12)(2004), 2094-2098.
  • [18] Hu, X.H., Cercone, N.: Learning in relational databases: a rough set approach. Computational Intelligence 11(2)(1995), 323-337.
  • [19] Ye, D.Y., Chen, Z.J.: A new discernibility matrix and the computation of a core. Acta Electronica Sinica 30(7)(2002), 1086-1088.
  • [20] Wang, G.Y.: Attribute Core of Decision Table. In: Alpigini J J, Peters J F, Skowron A, Zhong N Eds., Rough Sets and Current Trends in Computing (LNAI 2475), Springer-Verlag, 2002, 213-217.
  • [21] Wang, G.Y.: Calculation methods for core attributes of decision table. Chinese Journal of Computers 26(5)(2003), 611-615.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0008-0043
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