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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 706178, 8 pages
Research Article

Optimization ELM Based on Rough Set for Predicting the Label of Military Simulation Data

Science and Technology on Information Systems Engineering Laboratory, Nanjing 210007, China

Received 19 April 2014; Revised 26 July 2014; Accepted 18 August 2014; Published 25 September 2014

Academic Editor: Yi Jin

Copyright © 2014 Xiao-jian Ding and Ming Lei. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.