An Efficient Extreme Learning Machine Based on Fuzzy Information Granulation

Xia-fu LV, Jun-peng CHEN, Lei LIU, Bo-hua WANG, Yong WANG


In order to improve learning efficiency and generalization ability of extreme learning machine (ELM), an efficient extreme learning machine based on fuzzy information granulation (FIG) is put forward. Firstly, using FIG to get rid of redundant information in the original data set and then ELM is used to do train granulated data for prediction. This method not only improves the speed of basic ELM algorithm that contains many hidden nodes, but also overcomes the weakness of basic ELM of low learning efficiency and generalization ability by getting rid of redundant information in the observed values. The experimental results show that the proposed method is effective and can produce desirable generalization performance in most cases based on a few regression and classification problem.


Extreme learning machine (ELM), Fuzzy information granulation (FIG),Neural networks, Support vector machine (SVM)

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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