In machine learning, randomness is a crucial factor in the success of ensemble learning, and it can be injected into tree-based ensembles by rotating the feature space. However, it is a common practice to rotate the feature space randomly. Thus, a large number of trees are required to ensure the performance of the ensemble model. This random rotation method is theoretically feasible, but it requires massive computing resources, potentially restricting its applications. A multimodal genetic algorithm based rotation forest (MGARF) algorithm is proposed in this paper to solve this problem. It is a tree-based ensemble learning algorithm for classification, taking advantage of the characteristic of trees to inject randomness by feature rotation. However, this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization method. The classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification datasets. Experimental results show that the MGARF method outperforms the other methods, and the number of base learners in MGARF models is much fewer.

在机器学习中，随机性是集成学习成功的关键因素，它可以通过旋转特征空间注入到基于树的集成中。然而，随机旋转特征空间是一种常见的做法。因此，需要大量的树来确保集成模型的性能。这种随机旋转的方法在理论上是可行的，但它需要大量的计算资源，可能会限制其应用。针对这一问题，本文提出了一种基于多模态遗传算法的旋转森林（MGARF）算法。它是一种基于树的分类集成学习算法，利用树的特性通过特征旋转来注入随机性。然而，该算法尝试使用多模态优化方法选择更多样化和更准确的基础学习器的子集。通过将其与原始随机森林和随机旋转集成方法在 23 个 UCI 分类数据集上进行比较，评估所提出的 MGARF 算法的分类精度。实验结果表明，MGARF 方法优于其他方法，并且 MGARF 模型中的基学习器数量要少得多。