Heterogeneous Attribute Reduction in Noisy System based on a Generalized Neighborhood Rough Sets Model
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.
[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.
<http://www.ics.uci.edu/~mlearn/MLRepository.html>.
[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.
[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.
<http://www.ics.uci.edu/~mlearn/MLRepository.html>.
[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.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:56678", author = "Siyuan Jing and Kun She", title = "Heterogeneous Attribute Reduction in Noisy System based on a Generalized Neighborhood Rough Sets Model", 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", volume = "5", number = "3", pages = "379-6", }