Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans
Fig 3
Optimization and training of the two layers of SVM classification for pharyngeal grinder detection.
a) To construct the layer 1 classifier with the specified feature set, five-fold cross-validation with a manually annotated training set is first used to optimize SVM model parameters and ensure classification performance. b) Classification performance based on the false positive (FPR) and false negative (FNR) error rates observed in five-fold cross-validation allows selection of an optimal parameter set. c) The full training set and optimized parameters are used to construct the final layer 1 SVM model. Linear projections of the training set features onto two dimensions show that the layer 1 feature set and the optimized SVM model are insufficient for identifying the grinder particle with high specificity. d) The second layer of classification refines the final classification decision and is parameter-optimized using the candidates passed from layer 1 of classification. e) Classification performance based on five-fold cross-validation is used for parameter selection. f) The reduced layer 2 training set and optimized parameters are used to construct the final layer 2 SVM model. Linear projections of layer 2 features for the training set demonstrate the capability of a two layer scheme for the detection of the grinder with both high specificity and sensitivity.