To increase the diversity among the selected members to enhance the performance of the ensemble system, an ensemble Extreme Learning Machine (ELM) based on the selection of members similarity named EELMBSMS was proposed. Firstly, some candidate ELMs with high classification ability were selected. Then, Particle Swarm Optimization (PSO) algorithm was used to select the optimal subset of the ensemble members according to the similarity among the members. The diversity of the selected members was improved by selecting those ELMs with low similarity, which improved the classification performance of the ensemble system effectively. The selected ELMs obtained better performance with different integration rules. The experimental results on four UCI datasets verify that EELMBSMS has better stability and better generalization than some classical ensemble extreme learning machines.

Since traditional gene selection methods may select a large number of irrelevant genes, which leads to low sample prediction accuracy, a new hybrid method based on clustering and Particle Swarm Optimization (PSO) was proposed for gene selection of microarray data in this paper. Firstly, genes were partitioned into a certain number of clusters by using clustering algorithm. Then Extreme Learning Machine (ELM) was applied to validate the classification performance of the genes selected from each cluster, which formed an initial gene pool. Finally, the wrapper approach based on PSO and ELM was used to select compact gene subset with high classification accuracy from the initial gene pool. The better classification accuracy on microarray data was provided with the genes selected by the proposed method. The experiments on two public microarray data sets verify that the proposed method can obtain better classification performance with fewer genes than other classical methods.