Decision support in non-conservative domains: Generalization with neural networks

摘要

Models in conventional decision support systems (DSSs) are best suited for problem solutions in domains with well defined/structured (mathematical) or partially defined/ semi-structured (heuristic) domain models. Non-conservative/unstructured domains are those which either lack a known model or have a poorly defined domain model. Neural networks (NNs) represent an alternative modelling technique which can be useful in such domains. NNs autonomously learn the underlying domain model from examples and have the ability to generalize, i.e., use the learnt model to respond correctly to previously unseen inputs. This paper describes three different experiments to explore the use of NNs for providing decision support by generalization in non-conservative/unstructured domains. Our results indicate that NNs have the potential to provide adequate decision support in non-conservative/unstructured domains.