Analytical decision boundary feature extraction for neural networks

Recently, a feature extraction method based on decision boundary has been proposed for neural networks. The method is based on the fact that the vector normal to the decision boundary contains information useful for discriminating between classes. However, the normal vector was estimated numerically, resulting in inaccurate estimation and a long computational time. In this paper, we propose a new method to calculate the normal vector analytically and derive all the necessary equations for 3 layer feedforward neural networks with a sigmoid function. Experiments show that the proposed method provides a noticeably improved performance.