Abstract
The interval extreme learning machine (IELM) (Yang et al. in Neural Comput Appl 27(1):3–8, 2016) is a newly proposed regression algorithm to deal with the data with interval-valued inputs and interval-valued output. In this paper, we firstly analyze the disadvantages of IELM and further point out that IELM is actually a slight variant of fuzzy regression analysis using neural networks (Ishibuchi and Tanaka in Fuzzy Sets Syst 50(3):257–265, 1992). Then, we propose a new interval-valued ELM (IVELM) model to handle the interval-valued data regression. IVELM does not require any iterative adjustment to network weights and thus has the extremely fast training speed. The experimental results on data sets used in (Yang et al. 2016) demonstrate the feasibility and effectiveness of IVELM which obtains the better predictive performance and faster learning speed than IELM.
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Although the authors described the regression problem with multiple interval-valued outputs, in fact, they only used IELM to handle the regression problem with single interval-valued output. The experimental data sets used in IELM also confirmed this fact. Thus, there is only one interval-valued output which is considered in the following discussion.
References
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Acknowledgements
We thank the editor-in-chief and two anonymous reviewers for their valuable comments and suggestions. This work is supported by National Natural Science Foundations of China (61503252) and China Postdoctoral Science Foundations (2015M572361 and 2016T90799).
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Yang, Lf., Liu, C., Long, H. et al. Further improvements on extreme learning machine for interval neural network. Neural Comput & Applic 29, 311–318 (2018). https://doi.org/10.1007/s00521-016-2727-4
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DOI: https://doi.org/10.1007/s00521-016-2727-4