基于衍生的夏普利值，用于全局敏感性分析和机器学习的可解释性

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-05-01 DOI:10.1615/int.j.uncertaintyquantification.2024051548

Hui Duan, Giray Okten

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引用次数: 0

摘要

我们为全局敏感性分析和机器学习可解释性引入了一种新的夏普利值方法。该方法基于基础函数的一阶偏导数。该方法的计算复杂度与维度（特征数量）呈线性关系，而文献中其他夏普利值方法的计算复杂度呈指数关系。全局灵敏度分析和机器学习中的实例将用于对该方法与活动评分、SHAP 和 KernelSHAP 进行数值比较。

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Derivative-based Shapley value for global sensitivity analysis and machine learning explainability

We introduce a new Shapley value approach for global sensitivity analysis and machine learning explainability. The method is based on the first-order partial derivatives of the underlying function. The computational complexity of the method is linear in dimension (number of features), as opposed to the exponential complexity of other Shapley value approaches in the literature. Examples from global sensitivity analysis and machine learning are used to compare the method numerically with activity scores, SHAP, and KernelSHAP.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.