Logistic model tree extraction from artificial neural networks
Logistic model tree extraction from artificial neural networks
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DOI：
10.1109/tsmcb.2007.895334
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发表时间：
2007-08-01
影响因子：
--
通讯作者：
McLean, David
中科院分区：
文献类型：
--
作者：
Dancey, Darren;Bandar, Zuhair A.;McLean, David
人工神经网络(ANN)是一种功能强大、应用广泛的模式识别技术。然而，他们仍然是“黑匣子”，对他们所做的决定没有任何解释。本文提出了一种从神经网络中提取Logistic模型树(LMT)的新算法，它给出了隐藏在ANN中的知识的符号表示。Landwehr的LMT是基于标准决策树的，但终端节点被Logistic回归函数取代。本文报告了一种新的决策树提取算法与Quinlan的C4.5和ExTree进行比较的经验评估结果。这项评估使用了加州大学欧文分校机器学习储存库的12个标准基准数据集。评估结果表明，新算法生成的决策树比C4.5和Ext树生成的决策树具有更高的准确率和保真度。
Artificial neural networks (ANN's) are a powerful and widely used pattern recognition technique. However, they remain "black boxes" giving no explanation for the decisions they make. This paper presents a new algorithm for extracting a logistic model tree (LMT) from a neural network, which gives a symbolic representation of the knowledge hidden within the ANN. Landwehr's LMTs are based on standard decision trees, but the terminal nodes are replaced with logistic regression functions. This paper reports the results of an empirical evaluation that compares the new decision tree extraction algorithm with Quinlan's C4.5 and ExTree. The evaluation used 12 standard benchmark datasets from the University of California, Irvine machine-learning repository. The results of this evaluation demonstrate that the new algorithm produces decision trees that have higher accuracy and higher fidelity than decision trees created by both C4.5 and ExT ree.