|Appears in Collections:||Computing Science and Mathematics Book Chapters and Sections|
|Peer Review Status:||Refereed|
|Title:||The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus|
|Citation:||Ali R, Hussain A, Bron J & Shinn A (2012) The use of ASM feature extraction and machine learning for the discrimination of members of the fish ectoparasite genus gyrodactylus. In: Huang T, Zeng Z, Li C & Leung C (eds.) Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part IV. Lecture Notes in Computer Science, 7666. Berlin Heidelberg: Springer, pp. 256-263. http://link.springer.com/chapter/10.1007/978-3-642-34478-7_32#; https://doi.org/10.1007/978-3-642-34478-7_32|
machine learning classifier
|Series/Report no.:||Lecture Notes in Computer Science, 7666|
|Abstract:||Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their true species type. ASM is used as a feature extraction tool to select information from hook images that can be used as input data into trained classifiers. Linear (i.e. LDA and KNN) and non-linear (i.e. MLP and SVM) models are used to classify Gyrodactylus species. Species of Gyrodactylus, ectoparasitic monogenetic flukes of fish, are difficult to discriminate and identify on morphology alone and their speciation currently requires taxonomic expertise. The current exercise sets out to confidently classify species, which in this example includes a species which is notifiable pathogen of Atlantic salmon, to their true class with a high degree of accuracy. The findings from the current exercise demonstrates that data subsequently imported into a K-NN classifier, outperforms several other methods of classification (i.e. LDA, MLP and SVM) that were assessed, with an average classification accuracy of 98.75%.|
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