{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:19:25Z","timestamp":1784614765218,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T00:00:00Z","timestamp":1638403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"S\u00e3o Paulo Research Foundation","doi-asserted-by":"publisher","award":["2018\/20753-7"],"award-info":[{"award-number":["2018\/20753-7"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"S\u00e3o Paulo Research Foundation","doi-asserted-by":"publisher","award":["2018\/11953-2"],"award-info":[{"award-number":["2018\/11953-2"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Knowledge of animal behavior can be indicative of the well-being, health, productivity, and reproduction of animals. The use of accelerometers to classify and predict animal behavior can be a tool for continuous animal monitoring. Therefore, the aim of this study was to provide strategies for predicting more and less frequent beef cattle grazing behaviors. The behavior activities observed were grazing, ruminating, idle, water consumption frequency (WCF), feeding (supplementation) and walking. Three Machine Learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Na\u00efve Bayes Classifier (NBC) and two resample methods: under and over-sampling, were tested. Overall accuracy was higher for RF models trained with the over-sampled dataset. The greatest sensitivity (0.808) for the less frequent behavior (WCF) was observed in the RF algorithm trained with the under-sampled data. The SVM models only performed efficiently when classifying the most frequent behavior (idle). The greatest predictor in the NBC algorithm was for ruminating behavior, with the over-sampled training dataset. The results showed that the behaviors of the studied animals were classified with high accuracy and specificity when the RF algorithm trained with the resampling methods was used. Resampling training datasets is a strategy to be considered, especially when less frequent behaviors are of interest.<\/jats:p>","DOI":"10.3390\/ani11123438","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T21:19:08Z","timestamp":1638479948000},"page":"3438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Strategy to Predict High and Low Frequency Behaviors Using Triaxial Accelerometers in Grazing of Beef Cattle"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4034-0822","authenticated-orcid":false,"given":"Rafael N.","family":"Watanabe","sequence":"first","affiliation":[{"name":"Departamento de Engenharia e Ci\u00eancias Exatas, Universidade Estadual Paulista, Jaboticabal 14884-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priscila A.","family":"Bernardes","sequence":"additional","affiliation":[{"name":"Departamento de Zootecnia e Desenvolvimento Rural, Universidade Federal de Santa Catarina, Florian\u00f3polis 88034-000, Santa Catarina, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5109-3049","authenticated-orcid":false,"given":"Eli\u00e9der P.","family":"Romanzini","sequence":"additional","affiliation":[{"name":"Departamento de Zootecnia, Universidade Estadual Paulista, Jaboticabal 14884-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8374-5168","authenticated-orcid":false,"given":"Larissa G.","family":"Braga","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia e Ci\u00eancias Exatas, Universidade Estadual Paulista, Jaboticabal 14884-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tha\u00eds R.","family":"Brito","sequence":"additional","affiliation":[{"name":"Departamento de Zootecnia, Universidade Estadual Paulista, Jaboticabal 14884-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronyatta W.","family":"Teobaldo","sequence":"additional","affiliation":[{"name":"Departamento de Zootecnia, Universidade Estadual Paulista, Jaboticabal 14884-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4709-3094","authenticated-orcid":false,"given":"Ricardo A.","family":"Reis","sequence":"additional","affiliation":[{"name":"Departamento de Zootecnia, Universidade Estadual Paulista, Jaboticabal 14884-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6915-038X","authenticated-orcid":false,"given":"Dan\u00edsio P.","family":"Munari","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia e Ci\u00eancias Exatas, Universidade Estadual Paulista, Jaboticabal 14884-900, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.compag.2018.01.007","article-title":"Categorising sheep activity using a tri-axial accelerometer","volume":"145","author":"Barwick","year":"2018","journal-title":"Comput. 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