Ensemble pruning aims at attaining an ensemble composed of less size of leaners for improving classification ability. Extreme Learning Machine (ELM) is employed as a base learner in this work, in light of its salient features, an initial pool is constructed using ELM. An ensemble composed of ELMs with better performance and diversity can make it perform the best, but the average accuracy of the whole ELMs must be decreased as the increase of diversity among them. Hence there exists a balance between the diversity and the precision of ELMs. Existing works find it via diversity measures or heuristic algorithms, which cannot find the exact tradeoff. To solve the issue, ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization (EPEMBM) is proposed utilizing the integration of the proposed migratory binary glowworm swarm optimization (MBGSO) and margin distance minimization (MDM). First, the created ELMs in a pool can be pre-pruned by MDM, and it can markedly downsize the ELMs in the pool, and significantly alleviates its computation overhead. Second, the retaining ELMs are further pruned utilizing MBGSO, and the final ensemble is attained with a high efficiency. Experimental results on 21 UCI classification tasks indicate that EPEMBM outperforms techniques, and that its effectiveness and efficiency. It is a very useful tool for solving the selection problem of ELMs.

整体修剪旨在实现由较小规模的倾斜器组成的集合，以提高分类能力。极限学习机（ELM）被用作这项工作的基础学习者，鉴于其显着特征，使用ELM构建了初始池。由具有更好性能和多样性的ELM组成的集合可以使其表现最佳，但是随着它们之间多样性的增加，整个ELM的平均准确性必须降低。因此，ELM的多样性和精度之间存在平衡。现有作品是通过多样性测度或启发式算法找到的，无法找到确切的权衡。为了解决这个问题，利用迁移二进制萤火虫群优化（MBGSO）和边缘距离最小化（MDM）的集成，提出了通过迁移二进制萤火虫群优化和边缘距离最小化（EPEMBM）进行ELM的整体修剪。首先，可以通过MDM预先修剪池中创建的ELM，并且可以显着减小池中ELM的大小，并显着减轻其计算开销。其次，利用MBGSO对保留的ELM进行进一步修剪，并以高效率实现最终合奏。对21个UCI分类任务的实验结果表明EPEMBM优于技术，并且其有效性和效率。这是解决ELM选择问题的非常有用的工具。MDM可以预先修剪池中创建的ELM，并且可以显着减小池中ELM的大小，并显着减轻其计算开销。其次，利用MBGSO对保留的ELM进行进一步修剪，并以高效率实现最终合奏。在21个UCI分类任务上的实验结果表明EPEMBM优于技术，并且其有效性和效率。这是解决ELM选择问题的非常有用的工具。MDM可以预先修剪池中创建的ELM，并且可以显着减小池中ELM的大小，并显着减轻其计算开销。其次，利用MBGSO对保留的ELM进行进一步修剪，并以高效率实现最终合奏。对21个UCI分类任务的实验结果表明EPEMBM优于技术，并且其有效性和效率。这是解决ELM选择问题的非常有用的工具。以及其有效性和效率。这是解决ELM选择问题的非常有用的工具。以及其有效性和效率。这是解决ELM选择问题的非常有用的工具。