A decision-tree-based model for evaluating the thermal comfort of horses

Scientia Agricola

Ana Paula De Assis Maia²  Stanley Robson De Medeiros Oliveira²  Daniella Jorge De Moura²  Juliana Sarubbi¹  Rimena Do Amaral Vercellino²  Brenda Batista Lemos Medeiros²  Paulo Roberto Griska¹ 

[1] ,UNICAMP FEAGRI Campinas SP ,Brasil

关键词: feature selection methods;    data mining;    surface temperature;    infrared thermography;    thermoregulation;   

DOI  :  10.1590/S0103-90162013000600001

来源: SciELO

【 摘 要 】

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 (T S). This study aimed to assess the potential of data mining techniques as a tool to associate surface temperature with thermal comfort of horses. T S was obtained using infrared thermography image processing. Physiological and environmental variables were used to define the predicted class, which classified thermal comfort as "comfort" and "discomfort". The variables of armpit, croup, breast and groin T S of horses and the predicted classes were then subjected to a machine learning process. All variables in the dataset were considered relevant for the classification 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 classification using all attributes, armpit and breast T S 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.

【 授权许可】

CC BY   
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