The analysis of microscopic images of fish gonad cells (oocytes) is a useful tool
to estimate parameters of fish reproductive ecology and to analyze fish population dynamics.
The study of oocyte dynamics is needed to understand ovary development and reproduc-
tive cycle of fish. Oocytes go through different developmental states in a continuum temporal
sequence providing an interesting example of ordinal classification, which is not exploited by
the current oocyte analysis software. This promising paradigm of machine learning known as
ordinal classification or ordinal regression focus on classification problems where there exist
a natural order between the classes, thus requiring specific methods and evaluation metrics.
In this paper we compare 11 ordinal and 15 nominal state-of-the-art classifiers using oocytes
of three fish species (Merluccius merluccius, Trisopterus luscus and Reinhardtius hippoglos-
soides). The best results are achieved by SVMOD, an ordinal decomposition method of the
labelling space based on the Support Vector Machine, varying strongly with the number of
states for each specie (about 95 and 80 % of accuracy with three and six states respectively).
The classifiers designed specially for ordinal classification are able to capture the underlying
nature of the state ordering much better than common nominal classifiers. This is demon-
strated by several metrics specially designed to measure misclassification errors associated
to states far in the ranking scale.
Keywords: Fish oocytes, Ordinal classification, Texture analysis, Reinhardtius hippoglossoides, Decomposition methods