A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data.
A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data.
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DOI：
10.1371/journal.pone.0248261
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Garrard A
中科院分区：
文献类型：
--
作者：
Vos D;Stafford R;Jenkins EL;Garrard A
对考古特征的解释往往需要采用综合的方法，以便充分利用材料记录，特别是来自可能有限的遗址的材料记录。在实践中，这需要咨询不同的信息来源，以交叉验证调查结果，并解决模糊和不确定的问题。然而，多代理方法的应用程序通常会生成不兼容的数据，因此仍然可能提供模糊的结果。本文探讨了一个简单的数字框架的潜力，以增加多代理数据的解释能力，使不兼容的，模糊的数据集在一个单一的模型。为了实现这一点，贝叶斯确认与决策树结合使用。植硅体和地球化学分析的结果进行了短暂的网站在约旦的土壤样品作为一个案例研究。将这两个数据集作为单一模型的一部分相结合，使我们能够通过为某些活动区域提供另一种识别方法，来完善对考古遗址空间使用的初步解释。该模型的潜在应用范围要广得多，因为它还可以帮助其他领域的研究人员通过组合不同的数据集来对分析结果进行综合解释。
The interpretation of archaeological features often requires a combined methodological approach in order to make the most of the material record, particularly from sites where this may be limited. In practice, this requires the consultation of different sources of information in order to cross validate findings and combat issues of ambiguity and equifinality. However, the application of a multiproxy approach often generates incompatible data, and might therefore still provide ambiguous results. This paper explores the potential of a simple digital framework to increase the explanatory power of multiproxy data by enabling the incorporation of incompatible, ambiguous datasets in a single model. In order to achieve this, Bayesian confirmation was used in combination with decision trees. The results of phytolith and geochemical analyses carried out on soil samples from ephemeral sites in Jordan are used here as a case study. The combination of the two datasets as part of a single model enabled us to refine the initial interpretation of the use of space at the archaeological sites by providing an alternative identification for certain activity areas. The potential applications of this model are much broader, as it can also help researchers in other domains reach an integrated interpretation of analysis results by combining different datasets.
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影响因子：
1.7
作者：
Fernández, FG;Terry, RE;Eberl, M
通讯作者：
Eberl, M
影响因子：
2.8
作者：
Parnell, JJ;Terry, RE;Nelson, Z
通讯作者：
Nelson, Z
影响因子：
2.8
作者：
Albert, RM;Wiener, S;Meignen, L
通讯作者：
Meignen, L
影响因子：
2.5
作者：
Lancelotti, Carla;Ruiz-Perez, Javier;Jose Garcia-Granero, Juan
通讯作者：
Jose Garcia-Granero, Juan
DOI：
10.3406/paleo.1999.4689
发表时间：
2000-01-01
期刊：
Paleorient
影响因子：
--
作者：
Martin, Louise
通讯作者：
Martin, Louise