In order to improve the efficiency of disease diagnosis,a hybrid model of improved grey wolf optimization(IGWO)algorithm and kernel extreme learning machine(KELM)is proposed in this paper.By introducing a new mechanism to improve the exploration and exploitation abilities of grey wolf optimization algorithm.In addition to feature selection,the improved Grey Wolf optimization algorithm also optimizes two key parameters of the kernel extreme learning machine.The model was tested on two disease data sets.The experimental results show that the proposed model is about 1%-2%higher than other hybrid models in terms of accuracy,sensitivity and specificity,and the optimized model with feature selection is about 1%-2%higher than the model without feature selection in terms of evaluation met-rics.The results show that the proposed model has certain advantages.
关键词
灰狼优化算法/核极限学习机/疾病诊断/特征选择/参数优化
Key words
grey wolf optimization algorithm/kernel extreme learning machine/disease diagnosis/feature selection/parameter optimization
维普
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