Browsing by Subject "phylogenetic comparative methods"

Standard machine learning procedures are based on assumption that training and testing data is sampled independently from identical distributions. Comparative data of traits in biological species breaks this assumption. Data instances are related by ancestry relationships, that is phylogeny. In this study, new machine learning procedures are presented that take into account phylogenetic information when fitting predictive models. Phylogenetic statistics for classification accuracy and error are proposed based on the concept of effective sample size. Versions of perceptron training and KNN classification are built on these metrics. Procedures for regularised PGLS regression, phylogenetic KNN regression, neural network regression and regression trees are presented. Properties of phylogenetic perceptron training and KNN regression are studied with synthetic data. Experiments demonstrate that phylogenetic perceptron training improves robustness when the phylogeny is unbalanced. Regularised PGLS and KNN regression are applied to mammal dental traits and environments to both test the algorithms and gain insights in the relationship of mammal teeth and the environment.