An information geometric approach to survival analysis and feature selection by neural networks

An information geometric approach to survival analysis is described. It is shown how a neural network can be used to model the probability of failure of a system, and how it can be trained by minimising a suitable divergence functional in a Bayesian framework. By using the trained network, minimisation of the same divergence functional allows for fast, efficient and exact feature selection. Finally, the performance of the algorithms is illustrated on a synthetic dataset.