An Efficient Kernel Extreme Learning Machine Approach for Bankruptcy Prediction
DOI:
https://doi.org/10.54097/5xct7123Keywords:
Ente Kernel extreme learning machine, Parameter optimization, Fruit fly optimization, Bankruptcy prediction, Crisscross, Grey wolf optimizationAbstract
In recent years, many novel swarm intelligence algorithms have been proposed to apply in various fields. Kernel extreme learning machine (KELM) is combined kernel function with Extreme learning machine (ELM) algorithm. Then, KELM can reach the more robust and better generalization performance than the basic ELM. However, the performance of the method is determined by the crucial parameters in the practical cases. The key parameters of KELM are explored and the selection methods of the KELM-based swarm intelligence optimization are utilized. The FOA and GWO are intelligence computing methods which abstracts the math model from the hunting behavior of the creatures in the natural world. In order to further explore the ability of the FOA, crisscross strategy and GWO-based mechanism are introduced to FOA and called CGFOA. In this paper, a novel method of the CGFOA-KELM is proposed and successfully applied in financial bankruptcy prediction.
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