%0 Journal Article %A GAO Huiyun %A LU Huijuan %A YAN Ke %A YE Minchao %T Selective ensemble algorithm for gene expression data based on diversity and accuracy of weighted harmonic average measure %D 2018 %R 10.11772/j.issn.1001-9081.2017102464 %J Journal of Computer Applications %P 1512-1516 %V 38 %N 5 %X The diversity between base classifiers and the accuracy of single base classifiers itself are two important factors that affect the generalization performance of ensemble system. Aiming at the problem that the diversity and accuracy are difficult to balance, a selective ensemble algorithm for gene expression data based on Diversity and Accuracy of Weighted Harmonic Average (D-A-WHA) was proposed. The Kernel Extreme Learning Machine (KELM) was used as the base classifier, and the diversity and accuracy of base classifiers were adjusted by D-A-WHA measure. Finally, a set of classifiers with high accuracy and high diversity with other base classifiers were selected to ensemble. The experimental results on UCI gene dataset show that compared with traditional Bagging, Adaboost and other ensemble algorithms, the classification accuracy and stability of the selective ensemble algorithm based on D-A-WHA measure are improved significantly,and it can be applied to the classification of cancer gene expression data effectively. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2017102464