In ensemble learning, it is necessary to build a balancing mechanism to balance the accuracy of individual learners with the diversity between individual learners to achieve excellent ensemble learning performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, an ensemble learning approach based on balanced accuracy and diversity (ELBAD) that uses a two-phase artificial bee colony (ABC) algorithm is proposed to balance the accuracy and diversity of ensemble learners. In the first phase, the ABC algorithm is used to generate an ensemble classifier with appropriate diversity. In the second phase, the ABC algorithm is used to generate a weighted ensemble classifier. The ELBAD ensemble learning algorithm is significantly superior to other state-of-the-art popular ensemble learning algorithms, including AdaBoost, Bagging, Decorate, extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), and rotation forest (RoF) on 30 UCI datasets. In addition, this study proposes a systematic parameter tuning procedure for the ELBAD algorithm that reduces the time required to generate an ensemble classifier.

在整体学习中，有必要建立一种平衡机制，使个体学习者的准确性与个体学习者之间的多样性之间取得平衡，以实现出色的整体学习性能。在以前的研究中，多样性仅被视为正则化术语，这不足以表明多样性应隐含地视为准确性因子。在这项研究中，提出了一种基于平衡精度和多样性（ELBAD）的集成学习方法，该方法使用两阶段人工蜂群（ABC）算法来平衡集成学习者的准确性和多样性。在第一阶段，ABC算法用于生成具有适当分集的整体分类器。在第二阶段，使用ABC算法生成加权集成分类器。ELBAD集成学习算法明显优于其他最新的流行集成学习算法，包括AdaBoost，Bagging，Decorate，极随机树（ET），梯度提升决策树（GBDT），随机森林（RF），和30个UCI数据集上的旋转森林（RoF）。此外，这项研究为ELBAD算法提出了系统的参数调整程序，可减少生成集成分类器所需的时间。