Classification algorithm of loin swarm optimization kernel extreme learning machine - Liu Xinjian; Sun Zhonghua (Dianzi Jishu Yingyong)
Based on the classification and application of the kernel based extreme learning machine(KELM)，combined with the strong global optimization ability and fast convergence characteristics of the lion swarm optimization(LSO) algorithm, an LSO optimization KELM algorithm is proposed. The test accuracy is taken as the fitness function of LSO to optimize KELM, and the evaluation standard for data classification test is obtained according to the mobile position to obtain the optimal fitness value. Using UCI data set simulation test, the experimental results show that compared with KELM classification, LSO optimization KELM can obtain better classification accuracy. Compared with sparrow search algorithm(SSA) optimization KELM, LSO optimization KELM has faster convergence speed and better classification performance.