Data Farming Output Analysis Using Explainable AI
Data Farming Output Analysis Using Explainable AI
复制标题
使用可解释的人工智能进行数据农业输出分析
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Niclas Feldkamp
中科院分区：
文献类型：
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作者：
Niclas Feldkamp
数据农业将大规模模拟实验与高性能计算和复杂的大数据分析方法相结合。针对这些大量仿真数据的分析方法组合仍然具有进一步发展的潜力，新的方法不断涌现。尤其是机器学习和人工智能的应用是困难的，因为这些方法中的许多都非常擅长近似数据以进行预测，但不太擅长实际揭示其潜在的规则模型。为了克服这种黑盒算法缺乏可理解性的问题，一门名为可解释人工智能(XAI)的学科获得了很大的吸引力，最近变得非常流行。本文展示了如何使用XAI扩展数据农业产出分析方法的组合。
Data Farming combines large-scale simulation experiments with high performance computing and sophisticated big data analysis methods. The portfolio of analysis methods for those large amounts of simulation data still yields potential to further development, and new methods emerge frequently. Especially the application of machine learning and artificial intelligence is difficult, since a lot of those methods are very good at approximating data for prediction, but less at actually revealing their underlying model of rules. To overcome the lack of comprehensibility of such black-box algorithms, a discipline called explainable artificial intelligence (XAI) has gained a lot of traction and has become very popular recently. This paper shows how to extend the portfolio of Data Farming output analysis methods using XAI.
影响因子：
23.8
作者：
Lundberg, Scott M.;Erion, Gabriel;Lee, Su-In
通讯作者：
Lee, Su-In