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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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334089Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1261
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