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Optimization ELM Based on Rough Set for Predicting the Label of Military Simulation Data
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-25
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
By combining rough set theory with optimization extreme learning machine (OELM), a new hybrid machine learning technique is introduced for military simulation data classification in this study.
First, multivariate discretization method is implemented to convert continuous military simulation data into discrete data.
Then, rough set theory is employed to generate the simple rules and to remove irrelevant and redundant variables.
Finally, OELM is compared with classical extreme learning machine (ELM) and support vector machine (SVM) to evaluate the performance of both original and reduced military simulation datasets.
Experimental results demonstrate that, with the help of RS strategy, OELM can significantly improve the testing rate of military simulation data.
Additionally, OELM is less sensitive to model parameters and can be modeled easily.
American Psychological Association (APA)
Ding, Xiao-jian& Lei, Ming. 2014. Optimization ELM Based on Rough Set for Predicting the Label of Military Simulation Data. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1046399
Modern Language Association (MLA)
Ding, Xiao-jian& Lei, Ming. Optimization ELM Based on Rough Set for Predicting the Label of Military Simulation Data. Mathematical Problems in Engineering No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1046399
American Medical Association (AMA)
Ding, Xiao-jian& Lei, Ming. Optimization ELM Based on Rough Set for Predicting the Label of Military Simulation Data. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1046399
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1046399