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  1. International Journal of Machine Learning and Cybernetics
  2. International Journal of Machine Learning and Cybernetics : Volume 5
  3. International Journal of Machine Learning and Cybernetics : Volume 5, Issue 2, April 2014
  4. A rule-extraction framework under multigranulation rough sets
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International Journal of Machine Learning and Cybernetics : Volume 8
International Journal of Machine Learning and Cybernetics : Volume 7
International Journal of Machine Learning and Cybernetics : Volume 6
International Journal of Machine Learning and Cybernetics : Volume 5
International Journal of Machine Learning and Cybernetics : Volume 5, Issue 6, December 2014
International Journal of Machine Learning and Cybernetics : Volume 5, Issue 5, October 2014
International Journal of Machine Learning and Cybernetics : Volume 5, Issue 4, August 2014
International Journal of Machine Learning and Cybernetics : Volume 5, Issue 3, June 2014
International Journal of Machine Learning and Cybernetics : Volume 5, Issue 2, April 2014
Discovering the discovery of the No-Search Approach
Bayesian Citation-KNN with distance weighting
Sparse group LASSO based uncertain feature selection
Random fuzzy bilevel linear programming through possibility-based value at risk model
Reconstruction of surgical instruments in virtual surgery system
Topological approach to multigranulation rough sets
Gene ontology based quantitative index to select functionally diverse genes
Variable precision intuitionistic fuzzy rough sets model and its application
Improved particle swarm optimization based approach for bilevel programming problem-an application on supply chain model
Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device
Parallel quantum-behaved particle swarm optimization
A rule-extraction framework under multigranulation rough sets
International Journal of Machine Learning and Cybernetics : Volume 5, Issue 1, February 2014
International Journal of Machine Learning and Cybernetics : Volume 4
International Journal of Machine Learning and Cybernetics : Volume 3
International Journal of Machine Learning and Cybernetics : Volume 2
International Journal of Machine Learning and Cybernetics : Volume 1

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A rule-extraction framework under multigranulation rough sets

Content Provider Springer Nature Link
Author Liu, Xin Qian, Yuhua Liang, Jiye
Copyright Year 2013
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.
Starting Page 319
Ending Page 326
Page Count 8
File Format PDF
ISSN 18688071
Journal International Journal of Machine Learning and Cybernetics
Volume Number 5
Issue Number 2
e-ISSN 1868808X
Language English
Publisher Springer Berlin Heidelberg
Publisher Date 2013-08-24
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Multigranulation rough set Rule extraction Granulation selection Granule selection Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type Text
Resource Type Article
Subject Artificial Intelligence Computer Vision and Pattern Recognition Software
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