Multi-stage classification of Gyrodactylus species using machine learning and feature selection techniques

Abstract

This study explores the use of multi-stage machine learning based classifiers and feature selection techniques in the classification and identification of fish parasites. Accurate identification of pathogens is a key to their control and as a proof of concept, the monogenean worm genus Gyrodactylus, economically important pathogens of cultured fish species, an ideal test-bed for the selected techniques. Gyrodactylus salaris is a notifiable pathogen of salmonids and a semi-automated / automated method permitting its confident species discrimination from other non-pathogenic species is sought to assist disease diagnostics during periods of a suspected outbreak. This study will assist pathogen management in wild and cultured fish stocks, providing improvements in fish health and welfare and accompanying economic benefits. Multi-stage classification is proposed as a solution to this problem because use of a single classifier is not sufficient to ensure that all the species are accurately classified. The results show that Linear Discriminant Analysis (LDA) with 21 features is the best classifier for performing the initial classification of Gyrodactylus species. This first stage classification which allocates specimens to species-groups is then followed by a second or subsequent round of classification using additional classifiers to allocate species to their true class within the species-groups.