| Título |
Experiments with adabag in biology classification tasks |
| Autores |
Fernández-Delgado M. , Cernadas E. , PÉREZ ORTIZ, MARÍA |
| Publicación externa |
No |
| Medio |
Ensemble Classification Methods With Applications In R |
| Alcance |
Capítulo de un Libro |
| Naturaleza |
Científica |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104759434&doi=10.1002%2f9781119421566.ch6&partnerID=40&md5=c37bea5a921b045f76fcd1270804596f |
| Fecha de publicacion |
01/01/2018 |
| Scopus Id |
2-s2.0-85104759434 |
| DOI |
10.1002/9781119421566.ch6 |
| Abstract |
The assessment of fecundity is fundamental in the study of biology and to define the management of sustainable fisheries. Stereometry is an accurate method to estimate fecundity from histological images. This chapter shows some histological images of fish species Merluccius merluccius. The direct kernel perceptron (DKP) is a very simple and fast kernel-based classifier, whose trainable parameters are calculated directly, without any iterative training, using an analytical closed-form expression that involves only the training patterns and the classes to which they belong. An accurate fish fecundity estimation must only consider mature oocytes, which must be reliably classified, according to their stage of development, by experienced personnel using histological images. The fish oocytes were manually drawn and labelled with the development stage by expert technicians of the Institute of Marine Research CSIC using Govocitos software. Adaboost.M1 in Weka (ABW) is much worse than the Adabag version in all the species and experiments. © 2019 John Wiley & Sons, Ltd. |
| Palabras clave |
Direct kernel perceptron; Fecundity assessment; Govocitos software; Kernel-based classifier; Stereometry; Sustainable fisheries management |
| Miembros de la Universidad Loyola |
|
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