The ensemble learning methods have always been paid attention to their successful performance in handling supervised classification problems. Nevertheless, some deficiencies, such as inadequate diversity between classifiers and existing redundant classifiers, are among the main challenges in this kind of learning. In recent years, a method called density peak has been used in clustering methods to improve this process, which selects cluster centers from the local density peak. In this paper, inspiring this matter, and using the density peak criterion, a new method is proposed to create parallel ensembles. This criterion creates diverse training sets resulting in the generation of diverse classifiers. In the proposed method, during a multi-objective evolutionary decomposition-based optimization process, some (near) optimum diverse training datasets are created to improve the performance of the non-sequential ensemble learning methods. To do so, in addition to density peak as the first objective, the accuracy criterion is used as the second objective function. To show the superiority of the proposed method, it has been compared with the state-of-the-art methods over 19 datasets. To conduct a better comparison, non-parametric statistical tests are used, where the obtained results demonstrate that the proposed method can significantly dominate the other employed methods.

集成学习方法在处理监督分类问题中的成功表现一直受到关注。尽管如此，某些不足，例如分类器之间的多样性不足和现有的冗余分类器，仍是此类学习中的主要挑战。近年来，在聚类方法中已使用一种称为密度峰的方法来改进此过程，该方法从局部密度峰中选择聚类中心。在本文中，启发这个问题，并使用密度峰值准则，提出了一种新的方法来创建并行合奏。该标准创建了多种训练集，从而生成了多种分类器。在提出的方法中，在基于多目标进化分解的优化过程中，创建一些（近）最优的多样化训练数据集以提高非顺序集成学习方法的性能。为此，除了将密度峰值作为第一目标外，还将精度标准用作第二目标函数。为了显示所提出方法的优越性，已将其与19个数据集上的最新方法进行了比较。为了进行更好的比较，使用了非参数统计检验，其中获得的结果表明，所提出的方法可以显着地主导其他采用的方法。它已与19多个数据集的最新方法进行了比较。为了进行更好的比较，使用了非参数统计检验，其中获得的结果表明，所提出的方法可以显着地主导其他采用的方法。它已与19多个数据集的最新方法进行了比较。为了进行更好的比较，使用了非参数统计检验，其中获得的结果表明，所提出的方法可以显着地主导其他采用的方法。