基于加速度计的动物行为分类中监督机器学习模型验证的实用指南。

IF 3.7 1区 环境科学与生态学 Q1 ECOLOGY
Oakleigh Wilson, David Schoeman, Andrew Bradley, Christofer Clemente
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引用次数: 0

摘要

监督式机器学习已被用于从加速度计数据中检测精细尺度的动物行为,但目前缺乏实现该工作流程的标准化协议。随着机器学习应用于生态问题的扩展,建立与其他“大数据”领域一致的技术协议和验证标准至关重要。过拟合是机器学习中一个普遍且经常被误解的挑战。过度拟合模型过度适应训练数据来记忆特定的实例,而不是识别潜在的信号。相关结果可以表明训练集上的高性能,但这些模型不太可能推广到新数据。过拟合可以通过使用独立测试集的严格验证来检测。我们对119项使用基于加速度计的监督机器学习对动物行为进行分类的研究进行了系统回顾,结果显示,79%(94篇论文)的模型没有得到足够好的验证,无法稳健地识别潜在的过拟合。虽然这并不意味着这些模型是过拟合的,但缺乏独立的测试集限制了其结果的可解释性。为了解决这些挑战,我们提供了动物加速度测量背景下过拟合的理论概述,并提出了最佳验证技术的指导方针。我们的目标是为生态学家提供必要的工具,使一般机器学习验证理论适应生物学的特定要求,促进可靠的过拟合检测并推进该领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification

Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification

Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification

Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification

Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification

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来源期刊
Journal of Animal Ecology
Journal of Animal Ecology 环境科学-动物学
CiteScore
9.10
自引率
4.20%
发文量
188
审稿时长
3 months
期刊介绍: Journal of Animal Ecology publishes the best original research on all aspects of animal ecology, ranging from the molecular to the ecosystem level. These may be field, laboratory and theoretical studies utilising terrestrial, freshwater or marine systems.
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