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JPR Advance Access originally published online on October 22, 2008
Journal of Plankton Research 2009 31(1):19-29; doi:10.1093/plankt/fbn098
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© The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org

Optimizing the number of classes in automated zooplankton classification

Jose A. Fernandes1,2,*, Xabier Irigoien1, Guillermo Boyra1, Jose A. Lozano2 and Iñaki Inza2

1 AZTI-Tecnalia, Marine Research Division, Herrera Kaia Portualdea z/g, E-20110 Pasaia, Spain 2 Department of Computer Science and AI, University of the Basque Country, Intelligent Systems Group (ISG), Paseo Manuel de Lardizabal, 1, E-20018 Donostia–San SebastiÁn, Spain

* CORRESPONDING AUTHOR: jfernandes{at}pas.azti.es

Received on March 11, 2008; accepted on September 21, 2008


  Abstract

Zooplankton biomass and abundance estimation, based on surveys or time-series, is carried out routinely. Automated or semi-automated image analysis processes, combined with machine-learning techniques for the identification of plankton, have been proposed to assist in sample analysis. A difficulty in automated plankton recognition and classification systems is the selection of the number of classes. This selection can be formulated as a balance between the number of classes identified (zooplankton taxa) and performance (accuracy; correctly classified individuals). Here, a method is proposed to evaluate the impact of the number of selected classes, in terms of classification performance. On the basis of a data set of classified zooplankton images, a machine-learning method suggests groupings that improve the performance of the automated classification. The end-user can accept or reject these mergers, depending on their ecological value and the objectives of the research. This method permits both objectives to be equally balanced: (i) maximization of the number of classes and (ii) performance, guided by the end-user.

Corresponding editor: Roger Harris


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