%0 Conference Paper %1 magnussen2024adaptive %A Magnussen, Birk Martin %A Jessulat, Maik %A Stern, Claudius %A Sick, Bernhard %B International Conference on Big Data Analytics (ICBDA) %D 2024 %I IEEE %K imported itegpub isac-www explainable_ai big_data_mining shapley_values %P 305--310 %R 10.1109/ICBDA61153.2024.10607312 %T Adaptive Shapley: Using Explainable AI with Large Datasets to Quantify the Impact of Arbitrary Error Sources %X Almost all sensors suffer from some level of uncertainty introduced from production inaccuracies. When the sensor data is processed by machine learning, quantifying the impact of such production inaccuracies on the output of the machine learning model becomes difficult. Certain neural network architectures, such as continuous feature networks, allow individual features and data to be omitted while still being able to correctly predict the result without the need for retraining. Such features can, for example, be individual channels of a sensor. This article proposes a method to use the capability to omit arbitrary features or sensor channels to calculate Shapley values for each sensor channel. These Shapley values represent the contribution of each individual channel to the measurement. They are defined using an arbitrary function called the ``value function''. If the value function is defined as the error of the current measurement, the Shapley values will represent the contribution of each sensor channel to the error of the measurement result. By calculating Shapley values like this for a large unlabelled dataset of measurements, it is possible to understand how much measurement error was introduced by which channel of which sensor in each measurement. Averaging the Shapley values for each sensor in the dataset will then result in a metric for each channel of that sensor, which represents a contribution to measurement errors. By comparing these values to any arbitrary quality metrics for the sensor channels obtained in a calibration process or similar step, it is possible to correlate and quantify which value in the quality metric will cause how much of a measurement error, or whether the quality metric is even relevant for the measurement accuracy. This article will show the efficacy and use case of the method on an example of the production and quality control of optical sensors based on multiple spatially resolved reflection spectroscopy.

@inproceedings{magnussen2024adaptive, abstract = {Almost all sensors suffer from some level of uncertainty introduced from production inaccuracies. When the sensor data is processed by machine learning, quantifying the impact of such production inaccuracies on the output of the machine learning model becomes difficult. Certain neural network architectures, such as continuous feature networks, allow individual features and data to be omitted while still being able to correctly predict the result without the need for retraining. Such features can, for example, be individual channels of a sensor. This article proposes a method to use the capability to omit arbitrary features or sensor channels to calculate Shapley values for each sensor channel. These Shapley values represent the contribution of each individual channel to the measurement. They are defined using an arbitrary function called the ``value function''. If the value function is defined as the error of the current measurement, the Shapley values will represent the contribution of each sensor channel to the error of the measurement result. By calculating Shapley values like this for a large unlabelled dataset of measurements, it is possible to understand how much measurement error was introduced by which channel of which sensor in each measurement. Averaging the Shapley values for each sensor in the dataset will then result in a metric for each channel of that sensor, which represents a contribution to measurement errors. By comparing these values to any arbitrary quality metrics for the sensor channels obtained in a calibration process or similar step, it is possible to correlate and quantify which value in the quality metric will cause how much of a measurement error, or whether the quality metric is even relevant for the measurement accuracy. This article will show the efficacy and use case of the method on an example of the production and quality control of optical sensors based on multiple spatially resolved reflection spectroscopy.}, added-at = {2024-08-07T16:30:54.000+0200}, author = {Magnussen, Birk Martin and Jessulat, Maik and Stern, Claudius and Sick, Bernhard}, biburl = {https://www.bibsonomy.org/bibtex/21a6671c789facc1cdcc1e4d3ea4b1d4d/ies}, booktitle = {International Conference on Big Data Analytics (ICBDA)}, doi = {10.1109/ICBDA61153.2024.10607312}, interhash = {f09ad8129c185efdb4db7a29e7d44939}, intrahash = {1a6671c789facc1cdcc1e4d3ea4b1d4d}, keywords = {imported itegpub isac-www explainable_ai big_data_mining shapley_values}, pages = {305--310}, publisher = {IEEE}, timestamp = {2024-08-07T16:30:54.000+0200}, title = {Adaptive Shapley: Using Explainable AI with Large Datasets to Quantify the Impact of Arbitrary Error Sources}, year = 2024 }