This article investigates deep neural networks (DNNs)-based lung nodule classification with hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally expensive problem, and a surrogate-assisted evolutionary algorithm has been recently introduced to automatically search for optimal hyperparameter configurations of DNNs, by applying computationally efficient surrogate models to approximate the validation error function of hyperparameter configurations. Different from existing surrogate models adopting stationary covariance functions (kernels) to measure the difference between hyperparameter points, this article proposes a nonstationary kernel that allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs. A multilevel convolutional neural network (ML-CNN) is built for lung nodule classification, and the hyperparameter configuration is optimized by the proposed nonstationary kernel-based Gaussian surrogate model. Our algorithm searches with a surrogate for optimal setting via a hyperparameter importance-based evolutionary strategy, and the experiments demonstrate our algorithm outperforms manual tuning and several well-established hyperparameter optimization methods, including random search, grid search, the tree-structured parzen estimator (TPE) approach, Gaussian processes (GP) with stationary kernels, and the recently proposed hyperparameter optimization via RBF and dynamic (HORD) coordinate search.

中文翻译：

---

基于卷积神经网络的肺结节分类：一种用于超参数优化的替代辅助进化算法

本文通过超参数优化研究基于深度神经网络 (DNN) 的肺结节分类。DNN 中的超参数优化是一个计算成本很高的问题，最近引入了一种代理辅助进化算法，通过应用计算效率高的代理模型来近似超参数配置的验证误差函数，来自动搜索 DNN 的最佳超参数配置。与现有代理模型采用平稳协方差函数（核）来衡量超参数点之间的差异不同，本文提出了一种非平稳核，允许代理模型适应平滑度随输入空间位置变化的函数。为肺结节分类构建了多级卷积神经网络 (ML-CNN)，并通过所提出的基于非平稳内核的高斯代理模型优化了超参数配置。我们的算法通过基于超参数重要性的进化策略使用代理搜索最优设置，实验证明我们的算法优于手动调整和几种完善的超参数优化方法，包括随机搜索、网格搜索、树结构的 parzen 估计器（ TPE) 方法、具有固定内核的高斯过程 (GP) 以及最近提出的通过 RBF 和动态 (HORD) 坐标搜索进行的超参数优化。