|Appears in Collections:||Aquaculture Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||Multi-stage classification of Gyrodactylus species using machine learning and feature selection techniques|
|Citation:||Ali R, Hussain A, Bron J & Shinn A (2011) Multi-stage classification of Gyrodactylus species using machine learning and feature selection techniques Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA), Cordoba, Spain, 22.11.2011 - 24.11.2011, Piscataway, NJ, USA: IEEE, pp. 457-462.|
|Conference Name:||ISDA 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA)|
|Conference Location:||Cordoba, Spain|
|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.|
|Status:||Publisher version (final published refereed version)|
|Rights:||The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.|
|Affiliation:||University of Stirling|
Computing Science - CSM Dept
|Ali, Amir, Bron & Shinn 2011.pdf||210.89 kB||Adobe PDF||Under Embargo until 31/12/2999 Request a copy|
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