Abstract
Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input weights, the biases and the tradeoff factors are normally generated randomly or preset by human. Moreover, the number of input weights and biases depend on the size of hidden layer. Inappropriate quantity of hidden nodes may lead to the useless or redundant neuron nodes, and make the whole structure complex, even cause the worse generalization and unstable classification performances. Based on this, an adaptive CCR-ELM with variable-length brain storm optimization algorithm is proposed for the class imbalance learning. Each individual consists of all above parameters of CCR-ELM and its length varies with the number of hidden nodes. A novel mergence operator is presented to incorporate two parent individuals with different length and generate a new individual. The experimental results for nine imbalance datasets show that variable-length brain storm optimization algorithm can find better parameters of CCR-ELM, resulting in the better classification accuracy than other evolutionary optimization algorithms, such as GA, PSO, and VPSO. In addition, the classification performance of the proposed adaptive algorithm is relatively stable under varied imbalance ratios. Applying the proposed algorithm in the fault diagnosis of conveyor belt also proves that ACCR-ELM with VLen-BSO has the better classification performances.
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Acknowledgements
This work is supported by National Natural Science Foundation of China under Grant 61573361, Research Program of Frontier Discipline of China University of Mining and Technology under Grant 2015XKQY19, Six Talent Peak Project in Jiangsu Province under Grant 2017-DZXX-046 and National Key Research and Development Program under Grant 2016YFC0801406.
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Cheng, J., Chen, J., Guo, Yn. et al. Adaptive CCR-ELM with variable-length brain storm optimization algorithm for class-imbalance learning. Nat Comput 20, 11–22 (2021). https://doi.org/10.1007/s11047-019-09735-9
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DOI: https://doi.org/10.1007/s11047-019-09735-9