Novel sensitivity method for evaluating the first derivative of the feed-forward neural network outputs

• Main Authors: Ravi Kiran, Dayakar L. Naik
• Format: Article
• Language: English
• Published: SpringerOpen 2021-06-01
• Series: Journal of Big Data
• Subjects: Complex step derivative approximation (CSDA); Partial derivatives; Regression; Classification; Backpropagation; Forward propagation
• Online Access: https://doi.org/10.1186/s40537-021-00480-4

Abstract Evaluating the exact first derivative of a feedforward neural network (FFNN) output with respect to the input feature is pivotal for performing the sensitivity analysis of the trained neural network with respect to the inputs. In this paper, a novel method is presented that computes the analytical quality first derivative of a trained feedforward neural network output with respect to the input features without the need for backpropagation. To this end, the complex step derivative approximation is illustrated, and its implementation in the framework of the feedforward neural network is described. Artificial datasets are generated, and the efficacy of the proposed method for both regression and classification tasks is demonstrated. The results obtained for the regression task indicated that the proposed method is capable of obtaining analytical quality derivatives, and in the case of the classification task, the least relevant features could be identified.