Ensembles of wrappers for automated feature selection in fish age classification

In feature selection, the most important features must be chosen so as to decrease the number thereof while retaining their discriminatory information. Within this context, a novel feature selection method based on an ensemble of wrappers is proposed and applied for automatically select features in...

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Bibliographic Details
Published in:Computers and Electronics in Agriculture
Main Author: Bermejo Sánchez, Sergi
Other Authors: Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
Format: Article in Journal/Newspaper
Language:English
Published: 2017
Subjects:
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Agricultura
Agriculture > Data processing
Automated fish age classification
Atlantic cod otoliths
Feature selection
Nearest neighbor classifiers
Statistical pattern recognition
Support vector machines
Agricultura > Informàtica
atlantic cod
Online Access:http://hdl.handle.net/2117/100219
https://doi.org/10.1016/j.compag.2017.01.007
Description
Summary:In feature selection, the most important features must be chosen so as to decrease the number thereof while retaining their discriminatory information. Within this context, a novel feature selection method based on an ensemble of wrappers is proposed and applied for automatically select features in fish age classification. The effectiveness of this procedure using an Atlantic cod database has been tested for different powerful statistical learning classifiers. The subsets based on few features selected, e.g. otolith weight and fish weight, are particularly noticeable given current biological findings and practices in fishery research and the classification results obtained with them outperforms those of previous studies in which a manual feature selection was performed. Peer Reviewed Postprint (author's final draft)

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