@article{(Open Science Index):https://publications.waset.org/pdf/7597,
	  title     = {Heterogeneous Attribute Reduction in Noisy System based on a Generalized Neighborhood Rough Sets Model},
	  author    = {Siyuan Jing and  Kun She},
	  country	= {},
	  institution	= {},
	  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
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {5},
	  number    = {3},
	  year      = {2011},
	  pages     = {351 - 356},
	  ee        = {https://publications.waset.org/pdf/7597},
	  url   	= {https://publications.waset.org/vol/51},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 51, 2011},