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Open AccessArticle

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

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Department of Instrument Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Department of Applied Linguistics, University of Electronic Science and Technology of China, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(9), 912; https://doi.org/10.3390/e21090912
Received: 6 August 2019 / Revised: 4 September 2019 / Accepted: 12 September 2019 / Published: 19 September 2019
(This article belongs to the Special Issue Entropy Application for Forecasting)
In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed to provide a more robust training strategy based on the newly developed multi-kernel correntropy with the parameters that are generated using cooperative evolution. To achieve an accurate description of the correntropy, the method adopts a cooperative evolution which optimizes the bandwidths by switching delayed particle swarm optimization (SDPSO) and generates the corresponding influence coefficients that minimizes the minimum integrated error (MIE) to adaptively provide the best solution. The simulated experiments and real-world applications show that cooperative evolution can achieve the optimal solution which provides an accurate description on the probability distribution of the current error in the model. Therefore, the multi-kernel correntropy that is built with the optimal solution results in more robustness against the noise and outliers when training the model, which increases the accuracy of the predictions compared with other methods. View Full-Text
Keywords: correntropy; information theory extreme learning machine; evolved cooperation correntropy; information theory extreme learning machine; evolved cooperation
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Mei, W.; Liu, Z.; Su, Y.; Du, L.; Huang, J. Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction. Entropy 2019, 21, 912.

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