Please use this identifier to cite or link to this item:
Scopus Web of ScienceĀ® Altmetric
Type: Conference paper
Title: A rough-set based incremental approach for updating attribute reduction under dynamic incomplete decision systems
Author: Shu, W.
Shen, H.
Citation: Proceedings of the 2013 IEEE International Conference on Fuzzy Systems, FUZZ: pp.1-7
Publisher: IEEE
Publisher Place: Online
Issue Date: 2013
ISBN: 9781479900220
ISSN: 1098-7584
Conference Name: IEEE International Conference on Fuzzy Systems (2013 : Hyderabad, India)
Statement of
Wenhao Shu and Hong Shen
Abstract: Efficient attribute reduction in large-scale incomplete decision systems is a challenging problem. The computation of tolerance classes induced by the condition attributes in the incomplete decision system is a key part among all existing attribute reduction algorithms. Moreover, updating attribute reduction for dynamically-increasing decision systems has attracted much attention, in view of that incremental attribute reduction algorithms in a dynamic incomplete decision system have not yet been sufficiently discussed so far. In this paper, we first introduce a simpler way of computing tolerance classes than the classical method. Then we present an incremental attribute reduction algorithm to compute an attribute reduct for a dynamically-increasing incomplete decision system. Compared with the non-incremental algorithms, our incremental attribute reduction algorithm can compute a new attribute reduct in much shorter time. Experiments on four data sets downloaded from UCI show that the feasibility and effectiveness of the proposed incremental algorithm.
Keywords: Attribute reduction
Positive region
Incremental updating
Incomplete decision systems
Rough set theory
Rights: Copyright status unknown
DOI: 10.1109/FUZZ-IEEE.2013.6622431
Appears in Collections:Aurora harvest
Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.