Separating wheat from chaff: Diatom taxon selection using an artificial neural network pruning algorithm - Racca, JMJ; Wild, M; Birks, HJB; Prairie, YT (KLUWER ACADEMIC PUBL, 2003)
This study addresses the question of what diatom taxa to include in a modem calibration set based on their relative contribution in a palaeolinmological calibration model. Using a pruning algorithm for Artificial Neural Networks (ANNs) which determines the functionality of individual taxa in terms of model performance, we pruned the Surface Water Acidification Project (SWAP) pH-diatom data-set until the predictive performance of the pruned set (as assessed by a jackknifing procedure) was statistically different from the initial full-set. Our results, based on the validation at each 5% data-set reduction, show that (i) 85% of the taxa can be removed without any effect on the pH model calibration performance, and (ii) that the complexity and the dimensionality reduction of the model by the removal of these non-essential or redundant taxa greatly improve the robustness of the calibration. A comparison between the commonly used "marginal" criteria for inclusion (species tolerance and Hill's N2) and our functionality criterion shows that the importance of each taxon in an ANN palaeolimnological model calibration does not appear to depend on these marginal characteristics.