A novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network
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| Format: | Preprint |
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2024
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| _version_ | 1866912047871033344 |
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| author | Elborough, Lauren Taylor, Duncan Humphries, Melissa |
| author_facet | Elborough, Lauren Taylor, Duncan Humphries, Melissa |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_18156 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network Elborough, Lauren Taylor, Duncan Humphries, Melissa Quantitative Methods Machine Learning Genomics 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 |
| title | A novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network |
| topic | Quantitative Methods Machine Learning Genomics |
| url | https://arxiv.org/abs/2409.18156 |