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

摘要

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.