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Heterogeneous Attribute Reduction in Noisy System based on a Generalized Neighborhood Rough Sets Model

Authors: Siyuan Jing, Kun She

Abstract:

Neighborhood Rough Sets (NRS) has been proven to be an efficient tool for heterogeneous attribute reduction. However, most of researches are focused on dealing with complete and noiseless data. Factually, most of the information systems are noisy, namely, filled with incomplete data and inconsistent data. In this paper, we introduce a generalized neighborhood rough sets model, called VPTNRS, to deal with the problem of heterogeneous attribute reduction in noisy system. We generalize classical NRS model with tolerance neighborhood relation and the probabilistic theory. Furthermore, we use the neighborhood dependency to evaluate the significance of a subset of heterogeneous attributes and construct a forward greedy algorithm for attribute reduction based on it. Experimental results show that the model is efficient to deal with noisy data.

Keywords: attribute reduction, incomplete data, inconsistent data, tolerance neighborhood relation, rough sets

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334089

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References:


[1] Pawlak Z., "Rough sets.", Theoretical Aspects of Reasoning about Data, Kluwer, 1991.
[2] G. Y. Wang, "Rough Set Theory and Knowledge Discovery.", Xi-an:Xi-an Jiaotong University Press, 2001.
[3] C. Cornelis, M. De Cock, A. Radzikowska, "Vaguely Quantified Rough Sets,", Proc. 11th Int. Conf. on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC2007), Lecture Notes in Artificial Intelligence 4482, 2007, pp: 87-94.
[4] T. Y. Lin, Q. Liu, K J Huang, "Rough sets neighborhood systems and approximation.", In 15th International Symposium on Methodologies of Intelligent Systems, 1990.
[5] Y. Y. Yao, "Relational interpretation of neighborhood operators and rough set approximation operators.", Information sciences, vol. 111, no.198, pp: 239-259, 1998.
[6] W. Z. Wu, W. X. Zhang, "Neighborhood operator systems and approximations.", Information sciences, vol. 144, no.14, pp: 201-217, 2002.
[7] W. Ziarko, "Set approximation quality measures in the variable precision rough set model.", Soft Computing Systems, Management and Applications, pp: 442-452, 2001.
[8] Q. H. Hu, D. R. Yu, Z. X. Xie, "Numerical attribute reduction based on neighborhood granulation and rough approximation.", Chinese Journal of software, vol. 19, no.3, pp.640−649, 2008.
[9] M. R. Alicja, L. Rolka, "Variable Precision Fuzzy Rough Sets", Transaction on Rough Sets, LNCS, 144-160, 2004.
[10] D. J. Newman, S. Hettich, C. L. Blake, C. J. Merz, "UCI Repository of Machine Learning Databases.", University of California, Department of Information and Computer Science, Irvine, CA, 1998. .
[11] W. Ziarko, " Variable precision rough set model " , Journal of Computer and System Sciences, vol. 46, pp: 39-59, 1993.
[12] U. Fayyad, K. Irani, "Discrediting continuous attributes while learning Bayesian networks.", in 13th International Conference on Machine Learning, Morgan Kaufmann, 1996, pp: 157- 165.