论文标题
体重预测网络具有针对小样品表格生物医学数据的特征选择
Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data
论文作者
论文摘要
表格生物医学数据通常是高维的，但样品数量很少。尽管最近的工作表明，良好的简单神经网络可以在表格数据上胜过更复杂的体系结构，但它们仍然容易在具有许多可能无关的功能的微型数据集上过度拟合。为了解决这些问题，我们通过减少可学习参数的数量并同时执行特征选择，提出了使用功能选择（WPF）的权重预测网络（WPF），以从高维和小样本数据中学习神经网络。除了分类网络外，WPFS还使用两个小型辅助网络，共同输出分类模型第一层的权重。我们对九个现实世界的生物医学数据集进行了评估，并证明WPF的表现优于其他标准以及通常应用于表格数据的最新方法。此外，我们研究了提出的特征选择机制，并表明它可以提高性能，同时为学习任务提供有用的见解。
Tabular biomedical data is often high-dimensional but with a very small number of samples. Although recent work showed that well-regularised simple neural networks could outperform more sophisticated architectures on tabular data, they are still prone to overfitting on tiny datasets with many potentially irrelevant features. To combat these issues, we propose Weight Predictor Network with Feature Selection (WPFS) for learning neural networks from high-dimensional and small sample data by reducing the number of learnable parameters and simultaneously performing feature selection. In addition to the classification network, WPFS uses two small auxiliary networks that together output the weights of the first layer of the classification model. We evaluate on nine real-world biomedical datasets and demonstrate that WPFS outperforms other standard as well as more recent methods typically applied to tabular data. Furthermore, we investigate the proposed feature selection mechanism and show that it improves performance while providing useful insights into the learning task.
