Incremental Maintenance of Rough Fuzzy Set Approximations under the Variation of Object Set

• Autorzy: Zeng A. , Li T. , Zhang J. , Chen H.

Identyfikatory

DOI

10.3233/FI-2014-1051

• Języki publikacji: EN

Abstrakty

EN

The lower and upper approximations in rough set theory will change dynamically over time due to the variation of the information system. Incremental methods for updating approximations in rough set theory and its extensions have received much attention recently. Most existing incremental methods have difficulties in dealing with fuzzy decision systems which decision attributes are fuzzy. This paper introduces an incremental algorithm for updating approximations of rough fuzzy sets under the variation of the object set in fuzzy decision systems. In experiments on 6 data sets from UCI, comparisons of the incremental and non-incremental methods for updating approximations are conducted. The experimental results show that the incremental method effectively reduces the computational time.

Słowa kluczowe

EN

rough fuzzy sets; approximations; incremental learning; fuzzy decision system

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2014
• Tom: Vol. 132, nr 3
• Strony: 401--422
• Opis fizyczny: Bibliogr. 29 poz., tab., wykr.

Twórcy

autor

Zeng A.

• School of Information Science and Technology, Southwest Jiaotong Universit, Chengdu 610031, China

autor

Li T.

• School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China

autor

Zhang J.

• School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China

autor

Chen H.

• School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China

Bibliografia

• [1] Z. Pawlak: Rough sets. International Journal of Computer and Information Sciences, 11(5): 341-356 (1982).
• [2] D. Dubois, H. Prade: Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems, 17(2-3): 191-209 (1990).
• [3] Y. Yao: Combination of rough and fuzzy sets based on α-level sets. In: Proc. Rough Sets and Data Mining: Analysis for Imprecise Data, Lin, T.Y. and Cercone, N. (Eds.), Kluwer Academic Publishers, Boston, pp.301-321 (1997).
• [4] J. Zhang, T. Li, D. Ruan, Z. Gao, C. Zhao: A parallel method for computing rough set approximations, Information Sciences, 194: 209-223 (2012).
• [5] J. Zhang, T. Li, Y. Pan: Parallel rough set based knowledge acquisition using map reduce from big data. ACMSIGKDD12 Big Data Mining (BigMine’12) Workshop, Beijing, China, pp. 20-27 (2012).
• [6] C. Chan: A rough set approach to attribute generalization in data mining, Information Sciences, 107: 177-194 (1998).
• [7] T. Li, D. Ruan, G. Wets, J. Song, Y. Xu: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowledge-Based Systems, 20(5): 485-494 (2007).
• [8] J. Zhang, T. Li, D. Liu: An approach for incremental updating approximations in variable precision rough sets while attribute generalizing. In: Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, Hangzhou, China, pp. 17-81 (2010).
• [9] S. Li , T. Li, D. Liu: Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set. Knowledge-Based Systems, 40: 17-26 (2013).
• [10] C. Luo , T. Li, H. Chen: Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization. Information Sciences, 257: 210-228 (2014).
• [11] Y. Cheng: The incremental method for fast computing the rough fuzzy approximations. Data and Knowledge Engineering, 70: 84-100 (2011).
• [12] J. Zhang, T. Li, H. Chen: Composite rough sets for dynamic data mining. Information Sciences, 257: 81-100 (2014).
• [13] H. Chen, T. Li, S. Qiao, D. Ruan: A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values. International Journal of Intelligent Systems, 25: 1005-1026 (2010).
• [14] H. Chen, T. Li, D. Ruan: Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining, Knowledge-Based Systems, 31: 140-161 (2012).
• [15] H. Chen, T. Li, D. Ruan , J. Lin, C. X. Hu: A rough-set based incremental approach for updating approximations under dynamic maintenance environments. IEEE Transactions on Knowledge and Data Engineering,25(2): 274-284 (2013).
• [16] D. Liu, J. Zhang, T. Li: A probabilistic rough set approach for incremental learning knowledge on the change of attribute. In: Proceedings of 2010 International Conference on Foundations and Applications of Computational Intelligence, EMei, China, pp. 722-727 (2010).
• [17] D. Liu, T. Li, G. Liu, P. Hu: An incremental approach for inducing interesting knowledge based on the change of attribute values. In: Proceedings of 2009 IEEE International Conference on Granular Computing, Nanchang, China, pp. 415-418 (2009).
• [18] L. Shan, W. Ziarko, Data-based acquisition and incremental modification of classification rules. Computational Intelligence, 11: 357-370 (1995).
• [19] W. Bang, Z. Bien, New incremental learning algorithm in the framework of rough set theory. International Journal of Fuzzy Systems, 1: 25-36 (1999).
• [20] D. Liu, T. Li, D. Ruan, J. Zhang: Incremental learning optimization on knowledge discovery in dynamic business intelligent systems. Journal of Global Optimization, 51: 325-344 (2011).
• [21] Z. Zheng, G. Wang: A rough set and rule tree based incremental knowledge acquisition algorithm. Fundamenta Informaticae, 59(2-3): 299-313 (2004).
• [22] F. Hu, G. Wang, H. Huang, Y. Wu, Incremental attribute reduction based on elementary sets. In: Proceedings of 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, LNAI, 3641, Regina, Canada, pp. 185-193 (2005).
• [23] L. Wang, Y. Wu, G. Wang: An incremental rule acquisition algorithm based on variable precision rough set model. Journal of Chongqing University of Posts and Telecommunications, 17(6): 709-713 (2005).
• [24] J. Zhang, T. Li, D. Ruan, D. Liu: Neighborhood rough sets for dynamic data mining. International Journal of Intelligent Systems, 27: 317-342 (2012).
• [25] S. Li , T. Li, D. Liu: Dynamic maintenance of approximations in dominance-based rough Set approach under the variation of the object Set. International Journal of Intelligent Systems, 28 (8): 729-751 (2013).
• [26] C. Luo , T. Li, H. Chen, D. Liu: Incremental approaches for updating approximations in set-valued ordered information systems. Knowledge-Based Systems, 50: 218-233 (2013).
• [27] J. Zhang, J.-S. Wong, T. Li, Y. Pan: A comparison of parallel large-scale knowledge acquisition using rough set theory on different map Reduce runtime systems. International Journal of Approximate Reasoning, 55(3): 896-907 (2014).
• [28] D. Liu, T. R. Li, J. B. Zhang, An incremental approach for rule induction under coarsening and refining of attribute values in e-business systems. In: Proceedings of 2010 International Conference on Electronic-Business Intelligence, Kunming, China, pp. 541-547 (2010).
• [29] Z. Xu, Z. Liu, B. Yang, W. Song: A quick attribute reduction algorithm with complexity of max(O(|C||U|),O(|C|2|U/C|)). Chinese Journal of Computers, 29(3): 391-399 (2006) (in Chinese).