shapley values
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Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 487
Author(s):  
Bilin Shao ◽  
Yichuan Yan ◽  
Huibin Zeng

Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition must be modeled separately for prediction, which leads to overly complex models. To solve this problem, a VMD-WSLSTM load prediction model based on Shapley values is proposed in this paper. First, the Shapley value is used to select the optimal set of special features, and then the VMD decomposition method is used to decompose the original load into several smooth components. Finally, WSLSTM is used to predict each component. Unlike the traditional LSTM model, WSLSTM can simplify the prediction model and extract common features among the components by sharing the parameters among the components. In order to verify the effectiveness of the proposed model, several control groups were used for experiments. The results show that the proposed method has higher prediction accuracy and training speed compared with traditional prediction methods.


Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 83
Author(s):  
Aisha Aamir ◽  
Minija Tamosiunaite ◽  
Florentin Wörgötter

Deep neural networks (DNNs) dominate many tasks in the computer vision domain, but it is still difficult to understand and interpret the information contained within these networks. To gain better insight into how a network learns and operates, there is a strong need to visualize these complex structures, and this remains an important research direction. In this paper, we address the problem of how the interactive display of DNNs in a virtual reality (VR) setup can be used for general understanding and architectural assessment. We compiled a static library as a plugin for the Caffe framework in the Unity gaming engine. We used routines from this plugin to create and visualize a VR-based AlexNet architecture for an image classification task. Our layered interactive model allows the user to freely navigate back and forth within the network during visual exploration. To make the DNN model even more accessible, the user can select certain connections to understand the activity flow at a particular neuron. Our VR setup also allows users to hide the activation maps/filters or even interactively occlude certain features in an image in real-time. Furthermore, we added an interpretation module and reframed the Shapley values to give a deeper understanding of the different layers. Thus, this novel tool offers more direct access to network structures and results, and its immersive operation is especially instructive for both novices and experts in the field of DNNs.


2021 ◽  
Author(s):  
Anton Georgievich Voskresenskiy ◽  
Nikita Vladimirovich Bukhanov ◽  
Maria Alexandrovna Kuntsevich ◽  
Oksana Anatolievna Popova ◽  
Alexey Sergeevich Goncharov

Abstract We propose a methodology to improve rock type classification using machine learning (ML) techniques and to reveal causal inferences between reservoir quality and well log measurements. Rock type classification is an essential step in accurate reservoir modeling and forecasting. Machine learning approaches allow to automate rock type classification based on different well logs and core data. In order to choose the best model which does not progradate uncertainty further into the workflow it is important to interpret machine learning results. Feature importance and feature selection methods are usually employed for that. We propose an extension to existing approaches - model agnostic sensitivity algorithm based on Shapley values. The paper describes a full workflow to rock type prediction using well log data: from data preparation, model building, feature selection to causal inference analysis. We made ML models that classify rock types using well logs (sonic, gamma, density, photoelectric and resistivity) from 21 wells as predictors and conduct a causal inference analysis between reservoir quality and well logs responses using Shapley values (a concept from a game theory). As a result of feature selection, we obtained predictors which are statistically significant and at the same time relevant in causal relation context. Macro F1-score of the best obtained models for both cases is 0.79 and 0.85 respectively. It was found that the ML models can infer domain knowledge, which allows us to confirm the adequacy of the built ML model for rock types prediction. Our insight was to recognize the need to properly account for the underlying causal structure between the features and rock types in order to derive meaningful and relevant predictors that carry a significant amount of information contributing to the final outcome. Also, we demonstrate the robustness of revealed patterns by applying the Shapley values methodology to a number of ML models and show consistency in order of the most important predictors. Our analysis shows that machine learning classifiers gaining high accuracy tend to mimic physical principles behind different logging tools, in particular: the longer the travel time of an acoustic wave the higher probability that media is represented by reservoir rock and vice versa. On the contrary lower values of natural radioactivity and density of rock highlight the presence of a reservoir. The article presents causal inference analysis of ML classification models using Shapley values on 2 real-world reservoirs. The rock class labels from core data are used to train a supervised machine learning algorithm to predict classes from well log response. The aim of supervised learning is to label a small portion of a dataset and allow the algorithm to automate the rest. Such data-driven analysis may optimize well logging, coring, and core analysis programs. This algorithm can be extended to any other reservoir to improve rock type prediction. The novelty of the paper is that such analysis reveals the nature of decisions made by the ML model and allows to apply truly robust and reliable petrophysics-consistent ML models for rock type classification.


Author(s):  
Yunsheng Chen ◽  
Dionne M Aleman ◽  
Thomas G Purdie ◽  
Chris McIntosh

Abstract The complexity of generating radiotherapy treatments demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid clinically significant errors. Machine learning classifiers have been explored to augment the scope and efficiency of the traditional radiotherapy treatment planning QA process. However, one important gap in relying on classifiers for QA of radiotherapy treatment plans is the lack of understanding behind a specific classifier prediction. We develop explanation methods to understand the decisions of two automated QA classifiers: (1) a region of interest (ROI) segmentation/labeling classifier, and (2) a treatment plan acceptance classifier. For each classifier, a local interpretable model-agnostic explanation (LIME) framework and a novel adaption of team-based Shapley values framework are constructed. We test these methods in datasets for two radiotherapy treatment sites (prostate and breast), and demonstrate the importance of evaluating QA classifiers using interpretable machine learning approaches. We additionally develop a notion of explanation consistency to assess classifier performance. Our explanation method allows for easy visualization and human expert assessment of classifier decisions in radiotherapy QA. Notably, we find that our team-based Shapley approach is more consistent than LIME. The ability to explain and validate automated decision-making is critical in medical treatments. This analysis allows us to conclude that both QA classifiers are moderately trustworthy and can be used to confirm expert decisions, though the current QA classifiers should not be viewed as a replacement for the human QA process.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Iqbal Madakkatel ◽  
Ang Zhou ◽  
Mark D. McDonnell ◽  
Elina Hyppönen

AbstractWe present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using a Shapley values-based feature attribution method, SHAP values. Cox models controlled for false discovery rate were used for confounder adjustment, interpretability, and further validation. The pipeline was tested using information from 502,506 UK Biobank participants, aged 37–73 years at recruitment and followed over seven years for mortality registrations. From the 11,639 predictors included in GBDT, 193 potential risk factors had SHAP values ≥ 0.05, passed the correlation test, and were selected for further modelling. Of the total variable importance summed up, 60% was directly health related, and baseline characteristics, sociodemographics, and lifestyle factors each contributed about 10%. Cox models adjusted for baseline characteristics, showed evidence for an association with mortality for 166 out of the 193 predictors. These included mostly well-known risk factors (e.g., age, sex, ethnicity, education, material deprivation, smoking, physical activity, self-rated health, BMI, and many disease outcomes). For 19 predictors we saw evidence for an association in the unadjusted but not adjusted analyses, suggesting bias by confounding. Our GBDT-SHAP pipeline was able to identify relevant predictors ‘hidden’ within thousands of variables, providing an efficient and pragmatic solution for the first stage of hypothesis free risk factor identification.


Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1754
Author(s):  
Abdul Karim ◽  
Zheng Su ◽  
Phillip K. West ◽  
Matthew Keon ◽  
Jannah Shamsani ◽  
...  

Amyotrophic lateral sclerosis (ALS) is a prototypical neurodegenerative disease characterized by progressive degeneration of motor neurons to severely effect the functionality to control voluntary muscle movement. Most of the non-additive genetic aberrations responsible for ALS make its molecular classification very challenging along with limited sample size, curse of dimensionality, class imbalance and noise in the data. Deep learning methods have been successful in many other related areas but have low minority class accuracy and suffer from the lack of explainability when used directly with RNA expression features for ALS molecular classification. In this paper, we propose a deep-learning-based molecular ALS classification and interpretation framework. Our framework is based on training a convolution neural network (CNN) on images obtained from converting RNA expression values into pixels based on DeepInsight similarity technique. Then, we employed Shapley additive explanations (SHAP) to extract pixels with higher relevance to ALS classifications. These pixels were mapped back to the genes which made them up. This enabled us to classify ALS samples with high accuracy for a minority class along with identifying genes that might be playing an important role in ALS molecular classifications. Taken together with RNA expression images classified with CNN, our preliminary analysis of the genes identified by SHAP interpretation demonstrate the value of utilizing Machine Learning to perform molecular classification of ALS and uncover disease-associated genes.


2021 ◽  
Author(s):  
Daniel Deutch ◽  
Nave Frost ◽  
Amir Gilad ◽  
Oren Sheffer
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