Two‐Group Classification Using Neural Networks*
研究了神经网络在两组分类中的架构选择和训练样本量问题，并与线性判别分析、k近邻等方法比较，发现神经网络在训练样本中分类率不差，但在测试样本中不占优。
ABSTRACT Artificial neural networks are new methods for classification. We investigate two important issues in building neural network models; network architecture and size of training samples. Experiments were designed and carried out on two‐group classification problems to find answers to these model building questions. The first experiment deals with selection of architecture and sample size for different classification problems. Results show that choice of architecture and choice of sample size depend on the objective: to maximize the classification rate of training samples, or to maximize the generalizability of neural networks. The second experiment compares neural network models with classical models such as linear discriminant analysis and quadratic discriminant analysis, and nonparametric methods such as k ‐nearest‐neighbor and linear programming. Results show that neural networks are comparable to, if not better than, these other methods in terms of classification rates in the training samples but not in the test samples.