Subsampling Variance for Input Uncertainty Quantification

Lam, Henry; Qian, Huajie

doi:10.1109/wsc.2018.8632256

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…The subsampling layer can reduce the computation while ensuring the extraction of good features in the training process [ 26 ]. Max-pooling is adopted to obtain the maximum value in the data neighborhood to replace the features of the network of the upper layer.…”

Section: Methodsmentioning

confidence: 99%

Interpretation of Electrocardiogram Heartbeat by CNN and GRU

Yao

Mao

et al. 2021

Computational and Mathematical Methods in Medicine

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The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.

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Section: Methodsmentioning

confidence: 99%

Interpretation of Electrocardiogram Heartbeat by CNN and GRU

Yao

Mao

et al. 2021

Computational and Mathematical Methods in Medicine

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“…A sensitivity measure that fully reflects the dependence of the random vector of inputs is useful in applications where the dependence structure of the inputs is of particular interest, as in risk management applications (Glasserman and Xu, 2014;Lam, 2017). The computational costs of evaluating importance measures is a persistent theme in the sensitivity analysis literature (Saltelli et al, 2008;Lam and Qian, 2018), given the need for costly evaluations of the (high dimensional, non-linear) model function at different simulated scenarios. We provide explicit analytical representations of the cascade sensitivity, which do not require calculation of the gradient of the aggregation function and allow for a straightforward implementation on a single…”

Section: For Illustration Of Those Points We Consider a Proprietary mentioning

confidence: 99%

Cascade Sensitivity Measures

2018

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In risk analysis, sensitivity measures quantify the extent to which the probability distribution of a model output is affected by changes (stresses) in individual random input factors. For input factors that are statistically dependent, we argue that a stress on one input should also precipitate stresses in other input factors. We introduce a novel sensitivity measure, termed cascade sensitivity, defined as a derivative of a risk measure applied on the output, in the direction of an input factor. The derivative is taken after suitably transforming the random vector of inputs, thus enabling the cascade sensitivity measure to capture both the direct impact of the stressed input factor on the output, as well as indirect effects via other input factors that are dependent on the one being stressed. Alternative representations of the cascade sensitivity measure, which can be calculated from a single Monte Carlo sample, are provided for two types of stress: a) a perturbation of the distribution of an input factor such that the stressed input follows a mixture distribution, and b) an additive random shock applied to the input factor. The calculation of those representations does not require simulating under different model specifications or the explicit study of the properties of the model function, making the proposed method attractive for applications, as illustrated through numerical examples.

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“…A sensitivity measure that fully reflects the dependence of the random vector of inputs is useful in applications where the dependence structure of the inputs is of particular interest, as in risk management applications (Glasserman and Xu, 2014;Lam, 2017). Challenges to practical application of sensitivity analysis methods include the incomplete specification of (black-box) models, as well as high computational costs (Saltelli et al, 2008;Lam and Qian, 2018). We demonstrate how the decomposition of cascade sensitivities can be calculated using only bivariate copula information.…”

Section: Introduction 1overview and Contributionmentioning

confidence: 99%

Cascade Sensitivity Measures

2021

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In risk analysis, sensitivity measures quantify the extent to which the probability distribution of a model output is affected by changes (stresses) in individual random input factors.For input factors that are statistically dependent, we argue that a stress on one input should also precipitate stresses in other input factors. We introduce a novel sensitivity measure, termed cascade sensitivity, defined as a derivative of a risk measure applied on the output, in the direction of an input factor. The derivative is taken after suitably transforming the random vector of inputs, thus explicitly capturing the direct impact of the stressed input factor, as well as indirect effects via other inputs. Furthermore, alternative representations of the cascade sensitivity measure are derived, allowing us to address practical issues, such as incomplete specification of the model and high computational costs. The applicability of the methodology is illustrated through the analysis of a commercially used insurance risk model.

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Subsampling Variance for Input Uncertainty Quantification

Cited by 7 publications

References 37 publications

Interpretation of Electrocardiogram Heartbeat by CNN and GRU

Interpretation of Electrocardiogram Heartbeat by CNN and GRU

Cascade Sensitivity Measures

Cascade Sensitivity Measures

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