In recent years the neural networks have received considerable success on classification tasks. The randomly initialized network is generally expected to adjust the weights for reducing the loss, which is also indicated as the total distance from the samples to the corresponding vertices. However, the output space of the conventional neural networks suffers from the fixed relation between the labels and vertices during learning. This case forces the mapped points around the unexpected vertex across the decision boundary into the neighborhood of the correct vertex, and simultaneously the boundary points cannot obtain the substantial rectification. Therefore, this study proposes a novel nearest vertex attraction (NVA) to actively adjust the relation between the categories and the output vertices for improving the neural network classifiers. The best relation allows that the data points can be attracted by the nearest vertices to minimize the total moving distances. In this way, the mapped points that are near to the vertices and the decision boundaries obtain the decent management. We evaluated the NVA with several conventional classification techniques and other neural network classifiers on 12 public UCI datasets. The numerical experiments demonstrate that the proposed method improves performance of the neural network classifiers on the involved benchmarks.

近年来，神经网络在分类任务上取得了相当大的成功。随机初始化的网络通常期望调整权重以减少损失，这也表示为从样本到相应顶点的总距离。然而，传统神经网络的输出空间在学习过程中受到标签和顶点之间固定关系的影响。这种情况迫使跨越决策边界的意外顶点周围的映射点进入正确顶点的邻域，同时边界点无法获得实质性的校正。因此，本研究提出了一种新的最近顶点吸引（NVA）来主动调整类别与输出顶点之间的关系，以改进神经网络分类器。最佳关系允许数据点可以被最近的顶点吸引以最小化总移动距离。这样，靠近顶点和决策边界的映射点获得了体面的管理。我们在 12 个公共 UCI 数据集上使用几种传统分类技术和其他神经网络分类器评估了 NVA。数值实验表明，所提出的方法提高了神经网络分类器在所涉及的基准上的性能。我们在 12 个公共 UCI 数据集上使用几种传统分类技术和其他神经网络分类器评估了 NVA。数值实验表明，所提出的方法提高了神经网络分类器在所涉及的基准上的性能。我们在 12 个公共 UCI 数据集上使用几种传统分类技术和其他神经网络分类器评估了 NVA。数值实验表明，所提出的方法提高了神经网络分类器在所涉及的基准上的性能。