Guo H, Gelfand S B
Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN.
IEEE Trans Neural Netw. 1992;3(6):923-33. doi: 10.1109/72.165594.
The ideal use of small multilayer nets at the decision nodes of a binary classification tree to extract nonlinear features is proposed. The nets are trained and the tree is grown using a gradient-type learning algorithm in the multiclass case. The method improves on standard classification tree design methods in that it generally produces trees with lower error rates and fewer nodes. It also reduces the problems associated with training large unstructured nets and transfers the problem of selecting the size of the net to the simpler problem of finding a tree of the right size. An efficient tree pruning algorithm is proposed for this purpose. Trees constructed with the method and the CART method are compared on a waveform recognition problem and a handwritten character recognition problem. The approach demonstrates significant decrease in error rate and tree size. It also yields comparable error rates and shorter training times than a large multilayer net trained with backpropagation on the same problems.
提出了在二分类树的决策节点使用小型多层网络来提取非线性特征的理想方法。在多类情况下，使用梯度型学习算法对网络进行训练并生长树。该方法相对于标准分类树设计方法有所改进，因为它通常能生成错误率更低、节点更少的树。它还减少了与训练大型无结构网络相关的问题，并将选择网络大小的问题转化为寻找合适大小树的更简单问题。为此提出了一种有效的树剪枝算法。将用该方法构建的树与CART方法构建的树在波形识别问题和手写字符识别问题上进行比较。该方法显示出错误率和树大小的显著降低。与在相同问题上使用反向传播训练的大型多层网络相比，它还产生了相当的错误率和更短的训练时间。
