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

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 seriou...

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Vydáno v:2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) s. 160 - 166
Hlavní autoři: Zhu, Qingyu, Bai, Rui, Li, Mengting, Chen, Shaowei, Wen, Pengfei
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2019
Témata:
Bayes-OS-ELM
Classification algorithms
ensemble method
Fuses
Learning systems
Naive Bayes
Optimization
OS-ELM
parallel structure
Stability analysis
sub-classifier
Training
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Shrnutí: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.
DOI:10.1109/SDPC.2019.00037