Data-driven Valued Tolerance Relation Based on the Extended Rough Set

• Autorzy: Wang G. , Guan L. , Wu W. , Hu F.

Identyfikatory

DOI

10.3233/FI-2014-1048

• Języki publikacji: EN

Abstrakty

EN

The classical rough set theory is based on the conventional indiscernibility relation. It is not very good for analyzing incomplete information. Some successful extended rough set models based on different non-equivalence relations have been proposed. The valued tolerance relation is such an extended model of classical rough set theory. However, the general calculation method of tolerance degree needs to know the prior probability distribution of an information system in advance, and it is also difficult to select a suitable threshold. In this paper, a data-driven valued tolerance relation (DVT) is proposed to solve this problem based on the idea of data-driven data mining. The new calculation method of tolerance degree and the auto-selection method of threshold do not require any prior domain knowledge except the data set. Some properties about the DVT are analyzed. Experiment results show that the DVT can get better and more stable classification results than other extended models of the classical rough set theory.

Słowa kluczowe

EN

rough set; valued tolerance relation; data-driven

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2014
• Tom: Vol. 132, nr 3
• Strony: 349--363
• Opis fizyczny: Bibliogr. 22 poz., tab.

Twórcy

autor

Wang G.

• Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications Chongqing, P.R. China

autor

Guan L.

• Department of Mathematics, Chongqing Jiaotong University, Chongqing, P.R. China

autor

Wu W.

• School of Mathematics Physics and Information Science, Zhejiang Ocean University Zhoushan, Zhejiang, P.R. China

autor

Hu F.

• Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China

Bibliografia

• [1] Pawlak, Z.: Rough sets, International Journal of Computer and Information Sciences, 11, 1982, 341-356.
• [2] Grzymala-Busse, J. W., Hu, M.: A comparison of several approaches to missing attribute values in data mining, Proc. 2th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2000) (W. Ziarko, Y.Y. Yao, Eds.), LNCS 2005, 378-385, Springer-Verlag, Berlin, 2001.
• [3] Kryszkiewicz, M.: Rough set approach to incomplete information systems, Information Sciences, 112, 1998,39-49.
• [4] Slowinski, R., Vanderpooten, D.: A generalized definition of rough approximations based on similarity, IEEE Transactions on Knowledge and Data Engineering, 12(2), 2000, 331-336.
• [5] Wang, G. Y.: Extension of rough set under incomplete information systems, Journal of Computer Research and Development (in Chinese), 39(10), 2002, 1238-1243.
• [6] Stefanowski, J., Tsoukis, A.: On the extension of rough sets under incomplete information, Proc. 7th International Conference on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (RSFDGrC1999)(N. Zhong, A. Skowron, S. Ohsuga, Eds.), LNCS 1711, 73-81, Springer-Verlag, Berlin, 1999.
• [7] Grzymala-Busse, J. W.: Characteristic relations for incomplete data : a generalization of the indiscernibility relation, in: Transactions on Rough Sets IV (J.F. Peters, A. Skowron, Eds.), LNCS 3700, 58-68, Springer-Verlag, Berlin, 2005.
• [8] Greco, S., Slowinski, R., Zielniewicz, P.: Putting dominance-based rough set approach and robust ordinal regression together, Decision Support Systems, 54(2), 2013, 891-903.
• [9] Greco, S., Matarazzo, B., Slowinski, R.: The bipolar complemented de morgan brouwer-Zadeh distributive lattice as an algebraic structure for the dominance-based rough set approach, Fundamenta Informaticae,115(1), 2012, 25-56.
• [10] Wang, G.,Y., Guan, L.,H., Hu, F.: Rough set extensions in incomplete information systems, Frontiers of Electrical and Electronic Engineering in China, 3(4), 2008, 399-405.
• [11] Yao Y.,Y.: Two semantic issues in a probabilistic rough set model, Fundamenta Informaticae, 108(3-4), 2011, 249-265.
• [12] Wang, G. Y., Wang, Y.: 3DM: domain-oriented data-driven data mining, Fundamenta Informaticae, 90, 2009,395-426.
• [13] Wang, G. Y., He, X.: A self-learning model under uncertain conditions, Journal of Software(in Chinese),14(6),2003, 1096-1102.
• [14] Wang, Y., Shen, Y. X, Tao, C. M.: Domain-oriented data-driven knowledge acquisition model and its implementation, Journal of Chongqing University of Posts and Telecommunications (in Chinese), 21(4), 2008, 502-506.
• [15] Wang, Y., Wang, G. Y., Deng, W. B.: Concept lattice based data-driven uncertain knowledge acquisition, Pattern Recognition and Artificial Intelligence (in Chinese), 20(5), 2007, 626-642.
• [16] Blaszczyski, J., Deng, W. B., Hu, F., et al: On different ways of handling inconsistencies in ordinal classification with monotonicity constraints, Proc. 14th International Conference on Information Processing and Manacement of Uncertainty in Knowledge-Based Systems (IPMU 2012)(S. Greco, et al. Eds.), CCIS 297,300-309, Springer-Verlag, Berlin, 2012.
• [17] Guan, L. H, Wang, G. Y.: Generalized approximations defined by non-equivalence relations, Information Sciences, 193, 2012, 163-179.
• [18] Grzymala-Busse, J. W.: Rough set strategies to data with missing attribute values, in: Foundations and Novel Approaches in Data Mining, Studies in Computational Intelligence (T. Y. Lin, S. Ohsuga, C. J. Liau , X. H. Hu, Eds.), 9, 197-212, Springer-Verlag, Berlin, 2006.
• [19] Wang, G.Y., Hu, J.: Attribute reduction using extension of covering approximation space, Fundamenta Informaticae,115(2-3), 2012, 219-232.
• [20] Grzymala-Busse, J.W., Rzasa W.: Definability of approximations for a generalization of the indiscernibility relation, IEEE Symposium on Foundations of Computational Intelligence (FOCI2007), 65-72, Springer-Verlag, Berlin, 2007.
• [21] Yao, Y. Y., Zhang, N., Miao, D. Q, Xu, F. F: Set-theoretic approaches to granular computing, Fundamenta Informaticae,115(2-3), 2012, 247-264.
• [22] Clarkm P. G., Grzymala-Busse, J. W., Hippe, Z. S.: How good are probabilistic approximations for rule induction from data with missing attribute values? Proc. 8th International Conference of Rough Sets and Current Trends in Computing (RSCTC2012) (J. Yao et al, Eds.), LNCS 7413, 46-55, Springer-Verlag, Berlin, 2012