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Validation methods for plankton image classification systems
dc.contributor.author | Álvarez, Eva | |
dc.contributor.author | López Urrutia Lorente, Ángel | |
dc.contributor.author | Coz Velasco, Juan José del | |
dc.contributor.author | Díez Peláez, Jorge | |
dc.contributor.author | González, Pablo | |
dc.date.accessioned | 2017-03-24T07:58:50Z | |
dc.date.available | 2017-03-24T07:58:50Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Limnology and Oceanography: Methods, 3, p. 221-237 (2016); doi:10.1002/lom3.10151 | |
dc.identifier.issn | 1541-5856 | |
dc.identifier.uri | http://hdl.handle.net/10651/40997 | |
dc.description.abstract | In recent decades, the automatic study and analysis of plankton communities using imaging techniques has advanced significantly. The effectiveness of these automated systems appears to have improved, reaching acceptable levels of accuracy. However, plankton ecologists often find that classification systems do not work as well as expected when applied to new samples. This paper proposes a methodology to assess the efficacy of learned models which takes into account the fact that the data distribution (the plankton composition of the sample) can vary between the model building phase and the production phase. As opposed to most validation methods that consider the individual organism as the unit of validation, our approach uses a validation-by-sample, which is more appropriate when the objective is to estimate the abundance of different morphological groups. We argue that, in these cases, the base unit to correctly estimate the error is the sample, not the individual. Thus, model assessment processes require groups of samples with sufficient variability in order to provide precise error estimates | |
dc.description.sponsorship | MINECO (the Spanish Ministerio de Economía y Competitividad) and FEDER (Fondo Europeo de Desarrollo Regional), grant TIN2015-65069-C2-2-R. Juan José del Coz is also supported by the Fulbright Commission and the Salvador de Madariaga Program, grant PRX15/00607 | |
dc.language.iso | eng | |
dc.publisher | Wiley | |
dc.relation.ispartof | Limnology and Oceanography: Methods, 3 (2016) | |
dc.rights | © 2016 Wiley, Association for the Sciences of Limnology and Oceanography | |
dc.rights | CC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.title | Validation methods for plankton image classification systems | |
dc.type | journal article | |
dc.identifier.doi | 10.1002/lom3.10151 | |
dc.relation.projectID | MINECO-FEDER/TIN2015-65069-C2-2-R | |
dc.relation.publisherversion | http://dx.doi.org/10.1002/lom3.10151 | |
dc.rights.accessRights | open access | |
dc.type.hasVersion | SMUR |
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