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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2014

01.04.2014 | Original Article

A rule-extraction framework under multigranulation rough sets

verfasst von: Xin Liu, Yuhua Qian, Jiye Liang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2014

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Abstract

The multigranulation rough set (MGRS) is becoming a rising theory in rough set area, which offers a desirable theoretical method for problem solving under multigranulation environment. However, it is worth noticing that how to effectively extract decision rules in terms of multigranulation rough sets has not been more concerned. In order to address this issue, we firstly give a general rule-extraction framework through including granulation selection and granule selection in the context of MGRS. Then, two methods in the framework (i.e. a granulation selection method that employs a heuristic strategy for searching a minimal set of granular structures and a granule selection method constructed by an optimistic strategy for getting a set of granules with maximal covering property) are both presented. Finally, an experimental analysis shows the validity of the proposed rule-extraction framework in this paper.
Vorheriger Artikel Parallel quantum-behaved particle swarm optimization

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Fußnoten
1
The generalization is an important index of depicting classifier performance. In our study, we only discuss the lower approximation reduction. If we want to analyze the generalization of extracted rules in detail, we should take the upper approximation into account and design the corresponding multigranulation rough classifier. However, describing these contents in detail is beyond the scope of this paper. We will focus on the studies of the multigranulation rough classifier and its generalization in the future work.
 
2
The granulation selection in terms of MGRS is based on the model of multigranulation rough sets, which keeps the positive region in MGRS unchanged (i.e. the definition of approximation quality is based on multigranulation rough sets theory). The proposed granulation selection is different from the attribute reduction in terms of rough set.
 
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Metadaten
Titel
A rule-extraction framework under multigranulation rough sets
verfasst von
Xin Liu
Yuhua Qian
Jiye Liang
Publikationsdatum
01.04.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0194-0

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