Decision boundary feature extraction for neural networks

The authors propose a novel feature extraction method for neural networks. The method is based on the decision boundary feature extraction algorithm. It has been shown that all the necessary features for classification can be extracted from the decision boundary. To apply the method, the authors first define the decision boundary in neural networks. Next, they propose a procedure for extracting all the necessary features for classification from the decision boundary. The proposed algorithm preserves the characteristics of neural networks, which can define an arbitrary decision boundary. Experiments show promising results