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Improving Neural-Network Classifiers Using Nearest Neighbor Partitioning

机译：使用最近邻分区改进神经网络分类器

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摘要

This paper presents a nearest neighbor partitioning method designed to improve the performance of a neural-network classifier. For neural-network classifiers, usually the number, positions, and labels of centroids are fixed in partition space before training. However, that approach limits the search for potential neural networks during optimization; the quality of a neural network classifier is based on how clear the decision boundaries are between classes. Although attempts have been made to generate floating centroids automatically, these methods still tend to generate sphere-like partitions and cannot produce flexible decision boundaries. We propose the use of nearest neighbor classification in conjunction with a neural-network classifier. Instead of being bound by sphere-like boundaries (such as the case with centroid-based methods), the flexibility of nearest neighbors increases the chance of finding potential neural networks that have arbitrarily shaped boundaries in partition space. Experimental results demonstrate that the proposed method exhibits superior performance on accuracy and average f-measure.
机译：本文提出了一种旨在改善神经网络分类器性能的最近邻划分方法。对于神经网络分类器，通常在训练之前将质心的数量，位置和标签固定在分区空间中。但是，这种方法限制了在优化过程中对潜在神经网络的搜索。神经网络分类器的质量取决于类之间决策边界的清晰程度。尽管已尝试自动生成浮动质心，但是这些方法仍然倾向于生成球形的分区，并且无法生成灵活的决策边界。我们建议结合神经网络分类器使用最近邻分类。最近的邻居的灵活性而不是被球状的边界所束缚（例如，基于质心的方法），增加了找到潜在的神经网络的机会，这些神经网络在分区空间中具有任意形状的边界。实验结果表明，该方法在准确度和平均f测度上均表现出优异的性能。

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