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

Larrañaga, Ana and Bielza Lozoya, María Concepción and Pongrácz, Péter and Faragó, Tamás and Bálint, Anna and 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.

Description

Title: Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking
Author/s:
  • Larrañaga, Ana
  • Bielza Lozoya, María Concepción
  • Pongrácz, Péter
  • Faragó, Tamás
  • Bálint, Anna
  • Larrañaga Múgica, Pedro María
Item Type: Article
Título de Revista/Publicación: Animal cognition
Date: 2015
ISSN: 1435-9448
Volume: 18
Subjects:
Freetext Keywords: Mudi dog barks; Acoustic communication; Feature subset selection; Machine learning; Supervised classification; K-fold cross-validation
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7604102HBPEcole Polytechnique Federale de LausanneThe Human Brain Project
Government of SpainTIN2013-41592-PUnspecifiedUniversidad Politécnica de MadridAprendizaje de redes bayesianas con variables sin y con direccionalidad para descubrimiento de asociaciones, predicción multirespuesta y clustering
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain

More information

Item ID: 40923
DC Identifier: http://oa.upm.es/40923/
OAI Identifier: oai:oa.upm.es:40923
DOI: 10.1007/s10071-014-0811-7
Official URL: http://link.springer.com/article/10.1007/s10071-014-0811-7
Deposited by: Memoria Investigacion
Deposited on: 25 Oct 2016 07:52
Last Modified: 05 Jun 2019 17:47
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