外文摘要：Recent advancements in machine learning for detecting animal behaviors, particularly goat activities, have faced challenges due to their complexity and lack of explainability in practical applications. This article presents an interpretable machine-learning framework using sensor-based data to differentiate mimosa grazing from other goat activities like grazing herb, resting and walking. BORUTA, an algorithm for selecting the most relevant features, and SHAP, a technique for interpreting the decision of a machine learning model are two fundamental components of the methodology used for developing the model. The resulting model, a gradient boost algorithm with 15 selected features has shown robust performance with accuracy, sensitivity, and precision between 82% and 86%. SHAP analysis further elucidates the model's decision-making, highlighting the impact of features like 'Standing' and '%HeadDown,' along with distance-related features on discriminating grazing mimosa from grazing herb. The simplicity of the model advocates for its potential in real-time systems and underscores the importance of explainability in improving and deploying these models in real-world scenarios.
外文关键词：SHAP values;Animal behavior classification;Grazing goats;Machine learning explainability
作者：Lama, Sebastian Paez;Catania, Carlos;Ribeiro, Luana P;Puchala, Ryszard;Gipson, Terry A;Goetsch, Arthur L
作者单位：Argentinean Inst Arid Land Res;Amer Inst Goat Res;Natl Unversitiy Cuyo;Natl Sci & Tech Res Council
期刊名称：SMALL RUMINANT RESEARCH
期刊影响因子：0.0
出版年份：2024
出版刊次：233
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台