NDLI: On the Use of Nominal and Ordinal Classifiers for the Discrimination of States of Development in Fish Oocytes

Content Provider	Springer Nature Link
Author	Pérez Ortiz, M. Fernández Delgado, M. Cernadas, E. Domínguez Petit, R. Gutiérrez, P. A. Hervás Martínez, C.
Copyright Year	2015
Abstract	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 reproductive 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 hippoglossoides). 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 demonstrated by several metrics specially designed to measure misclassification errors associated to states far in the ranking scale.
Starting Page	555
Ending Page	570
Page Count	16
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	44
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2015-10-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Fish oocytes Ordinal classification Texture analysis Reinhardtius hippoglossoides Decomposition methods Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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