Genetic programming (GP) has been applied to feature learning for image classification and achieved promising results. However, many GP-based feature learning algorithms are computationally expensive due to a large number of expensive fitness evaluations, especially when using a large number of training instances/images. Instance selection aims to select a small subset of training instances, which can reduce the computational cost. Surrogate-assisted evolutionary algorithms often replace expensive fitness evaluations by building surrogate models. This article proposes an instance selection-based surrogate-assisted GP for fast feature learning in image classification. The instance selection method selects multiple small subsets of images from the original training set to form surrogate training sets of different sizes. The proposed approach gradually uses these surrogate training sets to reduce the overall computational cost using a static or dynamic strategy. At each generation, the proposed approach evaluates the entire population on the small surrogate training sets and only evaluates ten current best individuals on the entire training set. The features learned by the proposed approach are fed into linear support vector machines for classification. Extensive experiments show that the proposed approach can not only significantly reduce the computational cost but also improve the generalisation performance over the baseline method, which uses the entire training set for fitness evaluations, on 11 different image datasets. The comparisons with other state-of-the-art GP and non-GP methods further demonstrate the effectiveness of the proposed approach. Further analysis shows that using multiple surrogate training sets in the proposed approach achieves better performance than using a single surrogate training set and using a random instance selection method.

中文翻译：

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基于实例选择的代理辅助遗传编程用于图像分类中的特征学习


遗传编程（GP）已应用于图像分类的特征学习并取得了可喜的结果。然而，由于大量昂贵的适应度评估，许多基于 GP 的特征学习算法在计算上非常昂贵，特别是在使用大量训练实例/图像时。实例选择的目的是选择一小部分训练实例，这可以降低计算成本。替代辅助进化算法通常通过构建替代模型来取代昂贵的适应度评估。本文提出了一种基于实例选择的代理辅助 GP，用于图像分类中的快速特征学习。实例选择方法从原始训练集中选择多个小图像子集，形成不同大小的替代训练集。所提出的方法逐渐使用这些替代训练集，通过静态或动态策略来降低总体计算成本。在每一代，所提出的方法都会在小型替代训练集上评估整个群体，并且仅评估整个训练集上的十个当前最佳个体。通过所提出的方法学习到的特征被输入到线性支持向量机中进行分类。大量实验表明，所提出的方法不仅可以显着降低计算成本，而且比基线方法（使用整个训练集在 11 个不同的图像数据集上进行适应度评估）提高了泛化性能。与其他最先进的 GP 和非 GP 方法的比较进一步证明了该方法的有效性。 进一步的分析表明，在所提出的方法中使用多个代理训练集比使用单个代理训练集和使用随机实例选择方法获得了更好的性能。