Abstract
Kernel extreme learning machine (KELM) increases the robustness of extreme learning machine (ELM) by turning linearly non-separable data in a low dimensional space into a linearly separable one. However, the internal power parameters of ELM are initialized at random, causing the algorithm to be unstable. In this paper, we use the active operators particle swam optimization algorithm (APSO) to obtain an optimal set of initial parameters for KELM, thus creating an optimal KELM classifier named as APSO-KELM. Experiments on standard genetic datasets show that APSO-KELM has higher classification accuracy when being compared to the existing ELM, KELM, and these algorithms combining PSO/APSO with ELM/KELM, such as PSO-KELM, APSO-ELM, PSO-ELM, etc. Moreover, APSO-KELM has good stability and convergence, and is shown to be a reliable and effective classification algorithm.
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Acknowledgments
This study was partly supported by National Natural Science Foundation of China (No. 61272315, No. 61303183, No. 61572164, and No. 61402417),State Administration of Work Safety (zhejiang-0006-2014AQ), Zhejiang Provincial Natural Science Foundation (No. Y1110342, No. LQ13F020014, and No. Y1080950), Zhejiang Provincial Science and Technology Department of International Cooperation Project (No. 2012C24030). Thanks to Dr. Chen Pan, China Jiliang University.
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Lu, H., Du, B., Liu, J. et al. A kernel extreme learning machine algorithm based on improved particle swam optimization. Memetic Comp. 9, 121–128 (2017). https://doi.org/10.1007/s12293-016-0182-5
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DOI: https://doi.org/10.1007/s12293-016-0182-5