Application of the Back Propagation Neural Network Algorithm with Monotonicity Constraints for Two‐Group Classification Problems*
提出在反向传播学习算法中加入单调性约束，通过线性分类函数预处理训练样本，提升神经网络在特征向量与模式向量单调相关时的分类性能和效率，适用于商业等领域的分类问题。
ABSTRACT Neural network techniques are widely used in solving pattern recognition or classification problems. However, when statistical data are used in supervised training of a neural network employing the back‐propagation least mean square algorithm, the behavior of the classification boundary during training is often unpredictable. This research suggests the application of monotonicity constraints to the back propagation learning algorithm. When the training sample set is preprocessed by a linear classification function, neural network performance and efficiency can be improved in classification applications where the feature vector is related monotonically to the pattern vector. Since most classification problems in business possess monotonic properties, this technique is useful in those problems where any assumptions about the properties of the data are inappropriate.