Large variations across images make image classification a challenging task; limited training data further increases its difficulty. Genetic programming (GP) has been considerably applied to image classification. However, most GP methods tend to directly evolve a single classifier or depend on a predefined classification algorithm, which typically does not lead to ideal generalization performance when only a few training instances are available. Applying ensemble learning to classification often outperforms employing a single classifier. However, single-tree representation (each individual contains a single tree) is widely employed in GP. Training multiple diverse and accurate base learners/classifiers based on single-tree GP is challenging. Therefore, this article proposes a new ensemble construction method based on multi-tree GP (each individual contains multiple trees) for image classification. A single individual forms an ensemble, and its multiple trees constitute base learners. To find the best individual in which multiple trees are diverse and effectively cooperate, i.e., the nth tree can correct the errors of the previous n-1 trees, the new method assigns different weights to multiple trees using the idea of AdaBoost and performs classification via weighted majority voting. Furthermore, a new tree representation is developed to evolve diverse and accurate base learners that extract useful features and conduct classification simultaneously. The new approach achieves significantly better performance than almost all benchmark methods on eight datasets. Additional analyses highlight the effectiveness of the new ensembles and tree representation, demonstrating the potential for providing valuable interpretability in ensemble trees.

中文翻译：

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一种基于多树遗传编程的有限训练数据图像分类集成方法 [Research Frontier]


图像之间的巨大变化使图像分类成为一项具有挑战性的任务;有限的训练数据进一步增加了它的难度。遗传编程 （GP） 已广泛应用于图像分类。但是，大多数 GP 方法倾向于直接发展单个分类器或依赖于预定义的分类算法，当只有少数训练实例可用时，这通常不会带来理想的泛化性能。将集成学习应用于分类通常优于使用单个分类器。然而，单树表示（每个个体包含一棵树）在 GP 中被广泛使用。基于单树 GP 训练多个多样化且准确的基本学习器/分类器具有挑战性。因此，本文提出了一种基于多树 GP（每个个体包含多棵树）的一种新的集成构建方法进行图像分类。单个个体形成一个 ensemble，其多个树构成 base learners。为了找到多棵树多样化且有效合作的最佳个体，即第 n 棵树可以纠正前 n-1 棵树的错误，新方法使用 AdaBoost 的思想为多棵树分配不同的权重，并通过加权多数投票进行分类。此外，还开发了一种新的树表示，以发展多样化和准确的基本学习器，这些学习器可以提取有用的特征并同时进行分类。新方法在 8 个数据集上的性能明显优于几乎所有基准方法。其他分析强调了新集成和树表示的有效性，展示了在集成树中提供有价值的可解释性的潜力。