Determining the significance of input parameters using sensitivity analysis

Abstract

Accompanying the application of rule extraction algorithms to real-world problems is the crucial difficulty to compile a representative data set. Domain experts often find it difficult to identify all input parameters that have an influence on the outcome of the problem. In this paper we discuss the problem of identifying relevant input parameters from a set of potential input parameters. We show that sensitivity analysis applied to a trained feedforward neural network is an efficient tool for the identification of input parameters that have a significant influence on any one of the possible outcomes. We compare the results of a neural network sensitivity analysis tool with the results obtained from a machine learning algorithm, and discuss the benefits of sensitivity analysis to a neural network rule extraction algorithm.