Warianty tytułu
Języki publikacji
Abstrakty
For most attribute reduction in Pawlak rough set model (PRS), monotonicity is a basic property for the quantitative measure of an attribute set. Based on the monotonicity, a series of attribute reductions in Pawlak rough set model such as positive-region-preserved reductions and condition entropy-preserved reductions are defined and the corresponding heuristic algorithms are proposed in previous rough sets research. However, some quantitative measures of attribute set may be non-monotonic in probabilistic rough set model such as decision-theoretic rough set (DTRS), and the non-monotonic definition of the attribute reduction should be reinvestigated and the heuristic algorithm should be reconsidered. In this paper, the monotonicity of the positive region in PRS and DTRS are comparatively discussed. Theoretic analysis shows that the positive region in DTRS model may be expanded with the decrease of the attributes, which is essentially different from that in PRS model. Hereby, a new non-monotonic attribute reduction is presented for the DTRS model in this paper, and a heuristic algorithm for searching the newly defined attribute reduction is proposed, in which the positive region is allowed to be expanded instead of remaining unchanged in the process of attribute reduction. Experimental analysis is included to validate the theoretic analysis and quantify the effectiveness of the proposed attribute reduction algorithm.
Czasopismo
Rocznik
Tom
Strony
415--432
Opis fizyczny
Bibliogr. 38 poz., tab., wykr.
Twórcy
autor
- School of Management and Engineering, Nanjing University, Nanjing, P.R.China, huaxiongli@nju.edu.cn
autor
- School of Management and Engineering, Nanjing University, Nanjing, P.R.China, zhouxz@nju.edu.cn
autor
- School of Management and Engineering, Nanjing University, Nanjing, P.R.China, jbzhao@nju.edu.cn
autor
- School of Economics and Management, Southwest Jiaotong University, Chengdu, 610031, P.R. China, newton83@163.com
Bibliografia
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- [14] Li, W., Miao, D. Q., Wang, W. L., Zhang, N.: Hierarchical rough decision theoretic framework for text classification. Proceedings of ICCI’10, IEEE Press (2010) 484-489.
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- [16] Liu, D., Li, T. R., Hu, P., Li, H. X.: Multiple-category classification with decision-theoretic rough sets. Proceedings of RSKT’10, LNAI 6401, Springer, Berlin (2010) 703-710.
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- [34] Yu, H., Chu, S. S., Yang, D. C.: A Semiautonomous Clustering Algorithm Based on Decision-theoretic Rough Set Theory. Proceedings of ICCI’10, IEEE Press (2010) 477-483.
- [35] Yu, H., Chu, S. S., Yang, D. C.: Autonomous knowledge-oriented clustering using decision-theoretic rough set theory. Proceedings of RSKT’10, LNAI 6401, Springer, Berlin (2010) 687-694.
- [36] Zhao, Y., Luo, F., Wong, S. K. M., Yao, Y. Y.: A general definition of an attribute reduct. Proceedings of RSKT’07, LNAI 4481, Springer, Berlin (2007) 101-108.
- [37] Zhou, X. Z., Li, H. X.: A multi-view decision model based on decision-theoretic rough set. Proceedings of RSKT’09, LNCS 5589, Springer, Berlin (2009) 650-657.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-2dc569cb-41e6-4053-9e77-9300174bf803