Optimizing the number of classes in automated zooplankton classification

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.