School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Republic of Singapore.
Int J Neural Syst. 2011 Aug;21(4):265-76. doi: 10.1142/S0129065711002821.
While feedforward neural networks have been widely accepted as effective tools for solving classification problems, the issue of finding the best network architecture remains unresolved, particularly so in real-world problem settings. We address this issue in the context of credit card screening, where it is important to not only find a neural network with good predictive performance but also one that facilitates a clear explanation of how it produces its predictions. We show that minimal neural networks with as few as one hidden unit provide good predictive accuracy, while having the added advantage of making it easier to generate concise and comprehensible classification rules for the user. To further reduce model size, a novel approach is suggested in which network connections from the input units to this hidden unit are removed by a very straightaway pruning procedure. In terms of predictive accuracy, both the minimized neural networks and the rule sets generated from them are shown to compare favorably with other neural network based classifiers. The rules generated from the minimized neural networks are concise and thus easier to validate in a real-life setting.
虽然前馈神经网络已被广泛认为是解决分类问题的有效工具，但寻找最佳网络架构的问题仍然没有得到解决，特别是在实际问题设置中。我们在信用卡筛选的背景下解决了这个问题，在这种情况下，不仅要找到具有良好预测性能的神经网络，还要找到一个能够清晰解释其预测结果的神经网络。我们表明，具有一个隐藏单元的最小神经网络可以提供良好的预测准确性，同时具有一个额外的优点，即更容易为用户生成简洁易懂的分类规则。为了进一步减小模型大小，我们提出了一种新的方法，通过非常直接的修剪过程，从输入单元到隐藏单元的网络连接被删除。就预测准确性而言，最小化神经网络和从中生成的规则集都被证明与其他基于神经网络的分类器相比具有优势。从最小化神经网络生成的规则简洁明了，因此在实际环境中更容易验证。
