The Role of Genetic Programming in Describing the Microscopic Structure of Hydrating Plaster.
Judith Ellen Devaney, John G. Hagedorn · 2002
We apply genetic programming in conjunction with other machine learning methods to obtain concise rules that accurately identify scientifically meaningful components in hydrating plaster over multiple time periods. Genetic programming enables the derivation of understandable rules from otherwise opaque classifications. Our study was based on three dimensional data obtained through X-ray microtomography at five times in the hydration process. Starting with statistics based on locality and output from an unsupervised classification system (autoclass), we use genetic programming to derive simple rules for identifying three classes. These rules are tested on a separate subset of the plaster datasets that had been labeled with their autoclass predictions. The rules were found to have both high sensitivity and high positive predictive value. Genetic programming in conjunction with other machine learning methods enabled us to go from unlabeled data to simple classification rules in a straightforward manner. 1