A Neighborhood Rough Set Attribute Reduction Method Based on Attribute Importance ()
ABSTRACT
Attribute reduction is a hot topic in rough set research. As an extension
of rough sets, neighborhood rough sets can effectively solve the problem of
information loss after data discretization. However, traditional greedy-based
neighborhood rough set attribute reduction algorithms have a high computational
complexity and long processing time. In this paper, a novel
attribute reduction algorithm based on attribute importance is proposed. By
using conditional information, the
attribute reduction problem in neighborhood rough sets is discussed, and the
importance of attributes is measured by conditional information gain. The
algorithm iteratively removes the attribute with the lowest importance, thus achieving
the goal of attribute reduction. Six groups of UCI datasets are selected, and
the proposed algorithm SAR is compared with L2-ELM, LapTELM, CTSVM, and TBSVM classifiers. The
results demonstrate that SAR can effectively improve the time consumption and accuracy issues in attribute reduction.
Share and Cite:
Su, P. , Qin, F. and Li, F. (2023) A Neighborhood Rough Set Attribute Reduction Method Based on Attribute Importance.
American Journal of Computational Mathematics,
13, 578-593. doi:
10.4236/ajcm.2023.134031.
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