caa a22 4500 605542740 CHVBK 20210128100923.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s10071-014-0811-7 doi (NATIONALLICENCE)springer-10.1007/s10071-014-0811-7 Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking [Elektronische Daten] [Ana Larrañaga, Concha Bielza, Péter Pongrácz, Tamás Faragó, Anna Bálint, Pedro Larrañaga] 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, $$k$$ 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 $$K$$ 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 $$k$$ 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. Springer-Verlag Berlin Heidelberg, 2014 Mudi dog barks nationallicence Acoustic communication nationallicence Feature subset selection nationallicence Machine learning nationallicence Supervised classification nationallicence $$K$$ K -fold cross-validation nationallicence Larrañaga Ana Student at the Universidad Alfonso X El Sabio, Av. Universidad, 1, 28691, Villanueva de la Cañada, Madrid, Spain aut Bielza Concha Computational Intelligence Group, Universidad Politecnica de Madrid, Campus de Montegancedo, 28660, Boadilla del Monte, Madrid, Spain aut Pongrácz Péter Department of Ethology, Biological Institute, Eötvös Loránd University, 1117 Pázmány Péter sétány 1/c, Budapest, Hungary aut Faragó Tamás Department of Ethology, Biological Institute, Eötvös Loránd University, 1117 Pázmány Péter sétány 1/c, Budapest, Hungary aut Bálint Anna Department of Ethology, Biological Institute, Eötvös Loránd University, 1117 Pázmány Péter sétány 1/c, Budapest, Hungary aut Larrañaga Pedro Computational Intelligence Group, Universidad Politecnica de Madrid, Campus de Montegancedo, 28660, Boadilla del Monte, Madrid, Spain aut Animal Cognition Springer Berlin Heidelberg 18/2(2015-03-01), 405-421 1435-9448 18:2<405 2015 18 10071 https://doi.org/10.1007/s10071-014-0811-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10071-014-0811-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Larrañaga Ana Student at the Universidad Alfonso X El Sabio, Av. Universidad, 1, 28691, Villanueva de la Cañada, Madrid, Spain aut NATIONALLICENCE 700 1- Bielza Concha Computational Intelligence Group, Universidad Politecnica de Madrid, Campus de Montegancedo, 28660, Boadilla del Monte, Madrid, Spain aut NATIONALLICENCE 700 1- Pongrácz Péter Department of Ethology, Biological Institute, Eötvös Loránd University, 1117 Pázmány Péter sétány 1/c, Budapest, Hungary aut NATIONALLICENCE 700 1- Faragó Tamás Department of Ethology, Biological Institute, Eötvös Loránd University, 1117 Pázmány Péter sétány 1/c, Budapest, Hungary aut NATIONALLICENCE 700 1- Bálint Anna Department of Ethology, Biological Institute, Eötvös Loránd University, 1117 Pázmány Péter sétány 1/c, Budapest, Hungary aut NATIONALLICENCE 700 1- Larrañaga Pedro Computational Intelligence Group, Universidad Politecnica de Madrid, Campus de Montegancedo, 28660, Boadilla del Monte, Madrid, Spain aut NATIONALLICENCE 773 0- Animal Cognition Springer Berlin Heidelberg 18/2(2015-03-01), 405-421 1435-9448 18:2<405 2015 18 10071