Data/hora: 15/06/2024 17:51:30

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	04/02/2014	
Data da última atualização: 	06/02/2014	
Autoria: 	MAIA, A. P. de; OLIVEIRA, S. R. de M.; MOURA, D. J. de; SARUBBI, J.; VERCELLINO, R. do A.; MEDEIROS, B. B. L.; GRISKA, P. R.	
Afiliação: 	ANA PAULA DE ASSIS MAIA, Unicamp, Feagri; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; DANIELLA JORGE DE MOURA, Unicamp, Feagri; JULIANA SARUBBI, Universidade Federal de Santa Maria, RS; RIMENA DO AMARAL VERCELLINO, Unicamp, Feagri; BRENDA BATISTA LEMOS MEDEIROS, Unicamp, Feagri; PAULO ROBERTO GRISKA, Faculdade de Jaguariúna, SP.	
Título: 	A decision-tree-based model for evaluating the thermal comfort of horses.	
Ano de publicação: 	2013	
Fonte/Imprenta: 	Scientia Agricola, Piracicaba, v. 70, n. 6, p. 377-383, Nov./Dec. 2013.	
Idioma: 	Inglês	
Conteúdo: 	ABSTRACT: Thermal comfort is of great importance in preserving body temperature homeostasis during thermal stress conditions. Although the thermal comfort of horses has been widely studied, there is no report of its relationship with surface temperature (TS). This study aimed to assess the potential of data mining techniques as a tool to associate surface temperature with thermal comfort of horses. TS was obtained using infrared thermography image processing. Physiological and environmental variables were used to defi ne the predicted class, which classifi ed thermal comfort as ?comfort? and ?discomfort?. The variables of armpit, croup, breast and groin TS of horses and the predicted classes were then subjected to a machine learning process. All variables in the dataset were considered relevant for the classifi cation problem and the decision-tree model yielded an accuracy rate of 74 %. The feature selection methods used to reduce computational cost and simplify predictive learning decreased model accuracy to 70 %; however, the model became simpler with easily interpretable rules. For both these selection methods and for the classifi cation using all attributes, armpit and breast TS had a higher power rating for predicting thermal comfort. Data mining techniques show promise in the discovery of new variables associated with the thermal comfort of horses.	
Palavras-Chave: 	Conforto térmico de cavalos; Mineração de dados; Termografia.	
Thesagro: 	Termorregulação.	
Thesaurus Nal: 	Surface temperature; Thermoregulation.	
Categoria do assunto: 	S Ciências Biológicas	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/96318/1/2013-Scientia-data-mining-horses.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (17766-1) - UPC - AP - DD -  -  -  - DIS