Abstract Effective generating individual learners with strong generalization ability and great diversity is the key issue of ensemble learning. To improve diversity and accuracy of learners, Pairwise Diversity Measures based Selective Ensemble (PDMSEN) is proposed in this paper. Furthermore, an improved method is studied to advance the speed of the algorithm and support parallel computing. Finally, through applying BP neural networks as base learners, the experiment is carried out on selected UCI database and the improved algorithm is compared with Bagging and GASEN (Genetic Algorithm based Selected Ensemble) algorithms. Experimental results demonstrate that the learning speed of the proposed algorithm is superior to that of the GASEN algorithm with the same learning performance.
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