Shapley值在大型多维时间序列数据中的一种新应用：将可解释AI应用于DNA图谱分类神经网络
A novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network
AI总结：
本研究提出一种将超像素思想应用于Shapley值计算的高效方法，解决了高维时间序列数据的计算难题，并在法医DNA分类任务中实现了准确快速的解释。
摘要：
将Shapley值应用于高维、类似时间序列的数据在计算上具有挑战性，有时甚至是不可能的。对于$N$个输入，该问题的复杂度为$2^N$。在图像处理中，通常使用被称为超像素的像素簇来简化计算。本研究提出了一种针对类似时间序列数据的高效解决方案，该方案将超像素的思想应用于Shapley值计算。受法医DNA分类实例的启发，该方法被应用于由卷积神经网络（CNN）分类的多变量类似时间序列数据。在DNA处理中，从DNA提取和处理产生的背景噪声中识别等位基因非常重要。单个DNA图谱有$31,200$个扫描点需要分类，且分类决策必须在法庭上具有可辩护性。这意味着分类通常由人工读取者执行——这是一个巨大且耗时的过程。应用CNN并快速计算有意义的Shapley值为该分类提供了一种潜在的替代方案。本研究展示了这一大规模任务中Shapley值的现实、准确且快速的计算。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
