用超像素思想加速大规模时间序列的可解释性分析，用于法庭级DNA分类。
A novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network
- 将超像素思路引入Shapley值计算，降低高维时间序列分析复杂度
- 在31,200个扫描点的DNA数据上实现快速、准确的解释性分析
- 为司法鉴定中的人工读图提供自动化替代方案，适合法医与AI交叉研究者
将Shapley值应用于高维时序数据存在计算挑战——对于N个输入，问题复杂度为2^N。在图像处理中，通过将像素聚类为超像素来简化计算。本研究提出一种适用于时序类数据的高效方法，借鉴超像素思想优化Shapley值计算。以法医DNA分类为例，该方法应用于由卷积神经网络（CNN）处理的多变量时序数据。在DNA分析中，需从提取和处理产生的背景噪声中识别等位基因，单个DNA谱图包含31,200个扫描点，分类结果必须在法庭上可辩护，因此长期依赖人工判读，耗时巨大。本研究展示了一种可实现快速、真实且准确的Shapley值计算，为该任务提供了潜在的自动化替代方案。
原文摘要 · Abstract (English)
The application of Shapley values to high-dimensional, time-series-like data is computationally challenging - and sometimes impossible. For $N$ inputs the problem is $2^N$ hard. In image processing, clusters of pixels, referred to as superpixels, are used to streamline computations. This research presents an efficient solution for time-seres-like data that adapts the idea of superpixels for Shapley value computation. Motivated by a forensic DNA classification example, the method is applied to multivariate time-series-like data whose features have been classified by a convolutional neural network (CNN). In DNA processing, it is important to identify alleles from the background noise created by DNA extraction and processing. A single DNA profile has $31,200$ scan points to classify, and the classification decisions must be defensible in a court of law. This means that classification is routinely performed by human readers - a monumental and time consuming process. The application of a CNN with fast computation of meaningful Shapley values provides a potential alternative to the classification. This research demonstrates the realistic, accurate and fast computation of Shapley values for this massive task
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