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Methods of training set construction: Towards improving performance for automated mesozooplankton image classification systems

Continental Shelf Research
Publication Date
DOI: 10.1016/j.csr.2012.01.005
  • Balanced Training
  • Category-Specific Accuracy
  • Hydrographic Heterogeneity
  • Global Training Set
  • Water Mass-Specific Training Set
  • Zooscan
  • Chemistry
  • Earth Science
  • Ecology
  • Geography


Abstract The correspondence between variation in the physico-chemical properties of the water column and the taxonomic composition of zooplankton communities represents an important indicator of long-term and broad-scale change in marine systems. Evaluating and relating compositional change to various forms of perturbation demand routine taxonomic identification methods that can be applied rapidly and accurately. Traditional identification by human experts is accurate but very time-consuming. The application of automated image classification systems for plankton communities has emerged as a potential resolution to this limitation. The objective of this study is to evaluate how specific aspects of training set construction for the ZooScan system influenced our ability to relate variation in zooplankton taxonomic composition to variation of hydrographic properties in the East China Sea. Specifically, we compared the relative utility of zooplankton classifiers trained with the following: (i) water mass-specific and global training sets; (ii) balanced versus imbalanced training sets. The classification performance (accuracy and precision) of water-mass specific classifiers tended to decline with environmental dissimilarity, suggesting water-mass specificity However, similar classification performance was also achieved by training our system with samples representing all hydrographic sub-regions (i.e. a global classifier). After examining category-specific accuracy, we found that equal performance arises because the accuracy was mainly determined by dominant taxa. This apparently high classification accuracy was at the expense of accurate classification of rare taxa. To explore the basis for such biased classification, we trained our global classifier with an equal amount of training data for each category (balanced training). We found that balanced training had higher accuracy at recognizing rare taxa but low accuracy at abundant taxa. The errors introduced in recognition still pose a major challenge for automatic classification systems. In order to fully automate analyses of zooplankton communities and relate variation in composition to hydrographic properties, the recognition power of the system requires further improvements.

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