通过机器学习提高孤立的热足动物牙的分类:一项比较研究
Carolina S Marques1, Emmanuel Dufourq2,3,4, Soraia Pereira1,5
1Centro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
PeerJ
|March 31, 2025
概括
机器学习 (ML) 模型可以自动分类热足动物牙. 这项研究比较了ML技术,发现一些模型对不平衡的数据集更敏感,为准确的化石识别提供了解决方案.
科学领域:
- 古生物学的古生物学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 使用形态学变量对化石热足动物牙进行分类是具有挑战性的,因为时间限制和类内变异性.
- 机器学习 (ML) 为自动化孤立牙的分类识别提供了一个潜在的解决方案.
研究的目的:
- 为了比较六种ML技术的性能,以在属和更高的分类层分类类动物的牙.
- 评估数据标准化和过量采样技术对不平衡数据集的ML模型性能的影响.
- 根据它们在牙行中的位置来区分牙.
主要方法:
- 利用了来自隔离的热足动物牙的形态特征的数据集.
- 应用并比较了六种不同的ML分类技术.
- 研究了各种标准化和过量采样方法对不平衡数据集的影响.
主要成果:
- 在ML模型中,性能差异很大,有些模型对类不平衡的敏感性更高.
- 特定的标准化和过量采样技术提高了某些模型的分类准确性.
- 该研究确定了用于热足动物牙分类的最佳ML方法.
结论:
- 机器学习提供了一种可行的方法,用于自动化类动物牙分类.
- 通过适当的数据预处理来解决类失衡对于可靠的ML模型性能至关重要.
- 公共可用的模型和方法有助于未来的研究和古生物学的交叉研究比较.
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