A study on evolutionary design of binary decision trees
A study on evolutionary design of binary decision trees
复制标题
二元决策树的进化设计研究
DOI：
10.1109/cec.1999.785518
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
Mitsuyoshi Shirasaka
中科院分区：
文献类型：
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作者：
Qiangfu Zhao;Mitsuyoshi Shirasaka
对于模式识别，决策树 (DT) 比神经网络 (NN) 更有效，原因有两个。首先，决策的计算更加简单。其次，可以在设计过程中自动选择重要特征。然而，DT 不具有适应性。通过将 DT 映射到 NN 可以避免此问题。这种映射不仅使 DT 具有适应性，而且还提供了确定 NN 结构的系统方法。此外，由于特征经过精心选择，因此从该映射获得的神经网络的连接可能比直接设计的神经网络少得多。这里的关键是设计一个尽可能小的DT。我们研究了决策树的进化设计，并研究了一些提高设计效率的方法。
For pattern recognition, decision trees (DTs) are more efficient than neural networks (NNs) for two reasons. First, the computations in making decisions are simpler. Second, important features can be selected automatically during the design process. However, the DTs are not adaptable. This problem can be avoided by mapping a DT to an NN. This mapping not only makes a DT adaptable, but also provides a systematic way for determining the NN structure. In addition, since the features are well selected, the NN obtained from this mapping may have much fewer connections than those designed directly. The key point here is to design a DT which is as small as possible. We study the evolutionary design of the decision trees, and investigate some methods to improve the design efficiency.