A Study on Efficient Generation of Decision Trees Using Genetic Programming
A Study on Efficient Generation of Decision Trees Using Genetic Programming
复制标题
利用遗传编程高效生成决策树的研究
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
Qiangfu Zhao
中科院分区：
文献类型：
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作者：
Toru Tanigawa;Qiangfu Zhao
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。另一方面，神经网络具有适应性，因此具有在不断变化的环境中学习的能力。注意到从DT到NN有一个简单的映射，我们可以先设计一个DT，然后将其映射到NN。通过这样做，我们可以整合符号（DT）和子符号（NN）方法，并具有两者的优点。为此，我们应该设计尽可能小的DT。在本文中，我们继续研究基于遗传规划的决策树的进化设计，并提出了两种减少树大小的新方法。通过字符识别问题的实验来测试新方法的有效性。
For pattern recognition, the 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. On the other hand, NNs are adaptable, and thus have the ability to learn in changing environment. Noting that there is a simple mapping from DT to NN, we can design a DT first, and then map it to an NN. By so doing, we can integrate the symbolic (DT) and the sub-symbolic (NN) approaches, and have advantages of both. For this purpose, we should design DTs which are as small as possible. In this paper, we continue our study on the evolutionary design of the decision trees based on genetic programming, and propose two new methods to reduce the tree sizes. The effectiveness of the new methods are tested through experiments with a character recognition problem.