Dynamic Maintenance of Rough Fuzzy Approximations with the Variation of Objects and Attributes
In many fields including medical research, e-business and road transportation, data may vary over time, i.e., new objects and new attributes are added. In this paper, we present a method for dynamically updating approximations based on rough fuzzy sets under the variation of objects and attributes simultaneously in fuzzy decision systems. Firstly, a matrix-based approach is proposed to construct the rough fuzzy approximations on the basis of relation matrix. Then the method for incrementally computing approximations is presented, which involves the partition of the relation matrix and partly changes its element values based the prior matrices’ information. Finally, an illustrative example is employed to validate the effectiveness of the proposed method.
KeywordsFuzzy decision system Rough fuzzy set Incremental learning Matrix
This work is supported by the National Science Foundation of China (No. 61175047), NSAF (No. U1230117) and the Young Software Innovation Foundation of Sichuan Province, China (No. 2014-046).
- 2.Chen, H., Li, T., Luo, C., Horng, S., Wang, G.: A decision-theoretic rough set approach for dynamic data mining. IEEE Trans. Fuzzy Syst. PP(99), 1–1 (2015)Google Scholar
- 8.Karasuyama, M., Takeuchi, I.: Multiple incremental decremental learning of support vector machines. In: Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 907–915. MIT Press, Cambridge (2009)Google Scholar
- 18.Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.): Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Physica-Verlag GmbH, Heidelberg (2000)Google Scholar
- 19.Xu, W., Li, W.: Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans. Cybern. PP(99), 1–1 (2014)Google Scholar
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.