Bayes-OS-ELM :An Novel Ensemble Method For Classification Application

Abstract

Online Sequential Extreme Learning Machine (OS-ELM) has high accuracy and fast update speed in the areas of classification, such as fault diagnosis and anomaly detection. However, OS-ELM selects hidden layer parameters randomly leads to unstable output, which reduces the reliability of OS-ELM seriously. In this paper, a ensemble method based on OS-ELM and Naive Bayes(Bayes-OS-ELM) has been developed. The ensemble model establishes parallel sub-classifiers with OS-ELM and a secondary classifier with Naive Bayes to fuse the results of the former sub-classifiers. Because of the parallel structure, the ensemble model can greatly reduce the disturbance caused by the random set of hidden layer parameters of OS-ELM and make the classification result more stable. Besides, as an accurate and stable algorithm, Naive Bayes effectively promote the accuracy and stability of the classification model. Several UCI data sets have been involved to verify the proposed classification model. Experimental results show that this method has high accuracy, stable result and great generalization performance compared with the existing approach.