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

Content Provider	IEEE Xplore Digital Library
Author	Ali, R. Hussain, A. Bron, J.E. Shinn, A.P.
Copyright Year	2011
Description	Author affiliation: Institute of Computing Science and Mathematics, School of Natural Sciences, University of Stirling, Stirling FK9 4LA, SCOTLAND, UK (Ali, R.; Hussain, A.) \|\| Institute of Aquaculture, School of Natural Sciences, University of Stirling, Stirling FK9 4LA, SCOTLAND, UK (Bron, J.E.; Shinn, A.P.)
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.
Starting Page	457
Ending Page	462
File Size	326626
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121698
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Pathogens Gyrodactylus Scattering Machine learning Feature extraction machine learning Niobium species classification feature selection Aquaculture
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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