Abstract
The traditional weighted extreme learning machine chose input weights and hidden biases randomly. This made the algorithm responding slowly and led to worse generalization. In order to overcome the shortcomings, an improved weighted extreme learning machine combining with krill herd algorithm is proposed to solve the class imbalance problems. Krill herd algorithm is adopted to optimize the input weights and hidden biases. The simulation results show that krill herd algorithm can find more suitable input weights and hidden biases, which has better classify accuracy than particle swarm optimization and genetic algorithm. The proposed algorithm also has stable classify accuracy for the data sets with different imbalance ratios.
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Acknowledgments
This work is supported by National Natural Science Foundation of China under Grant 61573361, National Basic Research Program of China under Grant 2014CB046300 and the Innovation Team of CUMT under Grant 2015QN003. Also, thank you for the support from Collaborative Innovation Center of Intelligent Mining Equipment, CUMT.
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Guo, Yn. et al. (2017). An Improved Weighted ELM with Krill Herd Algorithm for Imbalanced Learning. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_39
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DOI: https://doi.org/10.1007/978-3-319-61833-3_39
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