Enhancing the robustness of a feedforward neural network in the presence of missing data
Date of Original Version
Statistical methods applied to real-world problems account for data that is known to be missing. In contrast, neural network designers often effectively ignore missing data, assigning zero or some other constant value, and letting the well-known robustness of the network handle it. We propose a novel technique which greatly enhances the correct decision rate for our given example. This scheme, which does not require prohibitive computational overhead, derives substitute values for the missing ones when their existence is known.
IEEE International Conference on Neural Networks - Conference Proceedings
Armitage, William D., and Jien Chung Lo. "Enhancing the robustness of a feedforward neural network in the presence of missing data." IEEE International Conference on Neural Networks - Conference Proceedings 2, (1994): 836-839. https://digitalcommons.uri.edu/ele_facpubs/825