Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking

Larrañaga, Ana; Bielza Lozoya, María Concepción; Pongrácz, Péter; Faragó, Tamás; Bálint, Anna y Larrañaga Múgica, Pedro María (2015). Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking. "Animal cognition", v. 18 (n. 2); pp. 405-421. ISSN 1435-9448. https://doi.org/10.1007/s10071-014-0811-7.

Descripción

Título: Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking
Autor/es:
  • Larrañaga, Ana
  • Bielza Lozoya, María Concepción
  • Pongrácz, Péter
  • Faragó, Tamás
  • Bálint, Anna
  • Larrañaga Múgica, Pedro María
Tipo de Documento: Artículo
Título de Revista/Publicación: Animal cognition
Fecha: 2015
Volumen: 18
Materias:
Palabras Clave Informales: Mudi dog barks; Acoustic communication; Feature subset selection; Machine learning; Supervised classification; K-fold cross-validation
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Barking is perhaps the most characteristic form of vocalization in dogs; however, very little is known about its role in the intraspecific communication of this species. Besides the obvious need for ethological research, both in the field and in the laboratory, the possible information content of barks can also be explored by computerized acoustic analyses. This study compares four different supervised learning methods (naive Bayes, classification trees, k-nearest neighbors and logistic regression) combined with three strategies for selecting variables (all variables, filter and wrapper feature subset selections) to classify Mudi dogs by sex, age, context and individual from their barks. The classification accuracy of the models obtained was estimated by means of K-fold cross-validation. Percentages of correct classifications were 85.13 % for determining sex, 80.25 % for predicting age (recodified as young, adult and old), 55.50 % for classifying contexts (seven situations) and 67.63 % for recognizing individuals (8 dogs), so the results are encouraging. The best-performing method was k-nearest neighbors following a wrapper feature selection approach. The results for classifying contexts and recognizing individual dogs were better with this method than they were for other approaches reported in the specialized literature. This is the first time that the sex and age of domestic dogs have been predicted with the help of sound analysis. This study shows that dog barks carry ample information regarding the caller’s indexical features. Our computerized analysis provides indirect proof that barks may serve as an important source of information for dogs as well.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
FP7604102HBPEcole Polytechnique Federale de LausanneThe Human Brain Project
Gobierno de EspañaTIN2013-41592-PSin especificarUniversidad Politécnica de MadridAprendizaje de redes bayesianas con variables sin y con direccionalidad para descubrimiento de asociaciones, predicción multirespuesta y clustering

Más información

ID de Registro: 40923
Identificador DC: http://oa.upm.es/40923/
Identificador OAI: oai:oa.upm.es:40923
Identificador DOI: 10.1007/s10071-014-0811-7
URL Oficial: http://link.springer.com/article/10.1007/s10071-014-0811-7
Depositado por: Memoria Investigacion
Depositado el: 25 Oct 2016 07:52
Ultima Modificación: 16 Nov 2016 09:03
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